Raising teacher education levels in Head Start: Exploring programmatic changes between 1999 and 2011

Early Childhood Research Quarterly 28 (2013) 831–842

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Early Childhood Research Quarterly

Raising teacher education levels in Head Start: Exploring programmatic changes between 1999 and 2011 Daphna Bassok ∗ Curry School of Education, 405 Emmet Street South, P.O. Box 400277, Charlottesville, VA 22904, United States

a r t i c l e

i n f o

Article history: Received 16 December 2010 Received in revised form 16 July 2013 Accepted 17 July 2013 Keywords: Teacher education Head Start Teacher quality

a b s t r a c t Between 1999 and 2011, the percentage of Head Start teachers nationwide with an Associate’s Degree or higher more than doubled from 38 to 85%. Over the same period, the percentage of teachers with a BA also rose rapidly from 23 to 52%. This paper uses within-program fixed-effects models and a 13-year panel of administrative data on all Head Start programs in the United States to explore whether programs that experienced increases in teacher education experienced changes with respect to comprehensive service provision, staffing choices and the racial composition of the staff. I find no evidence that programs that raised their teachers’ education levels sacrificed health or social services. However, programs with gains in teacher education did see some increases in child–teacher ratios, turnover, and racial divergence between children and staff, which may be associated negatively with young children’s development. © 2013 Elsevier Inc. All rights reserved.

Over the past decade, more and more states have mandated higher educational credentials for early childhood educators. The 2007 reauthorization of the federal Head Start preschool program required that by 2013, half of all lead teachers in the program hold a Baccalaureate degree (BA) in early childhood or a related field. Similarly, 24 states currently require lead teachers in public preschools to have a BA (Barnett, Carolan, Fitzgerald, & Squires, 2011). Training, attracting, and retaining more educated teachers is costly. Particularly given constrained budgets, mandates for higher teacher qualifications may yield important programmatic changes. To date, the early childhood literature on teacher education has focused on the direct effects of teacher education on classroom quality and children’s learning (e.g. Are teachers with a degree in early childhood more sensitive in their interactions with children? Do their students learn more?). However, to assess accurately the potential impact of degree mandates, it is necessary to consider not only the direct effects of employing teachers with higher credentials but any unintended consequences and their costs and benefits as well. This paper leverages a 13-year panel of administrative data from Head Start to explore whether Head Start programs that experienced changes in teacher education levels also saw meaningful programmatic changes along several other dimensions. The 1998 reauthorization of Head Start mandated that by 2003, 50% of all Head Start teachers nationwide have, at minimum, an Associate’s degree (AA) in early childhood education or a related field. While

∗ Tel.: +1 434 982 5415; fax: +1 434 924 3866. E-mail address: [email protected] 0885-2006/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.ecresq.2013.07.004

in 1999, only 38% of Head Start teachers had an AA or above, by 2011, this figure had more than doubled to 85%. This change occurred during a period largely characterized by stable or declining funding for the Head Start program. It therefore provides a unique opportunity to explore timely questions about efforts to maintain and improve the quality of early childhood programs while facing strained budgets. The current study measures to what extent increases in Head Start teachers’ education levels are associated with meaningful changes along three other dimensions; (1) the provision of non-academic services such as health and social services; (2) staff structure and stability (i.e. child–teacher ratios, teacher turnover) and (3) the racial composition of the teaching force relative to the student body. 1. Why mandate higher education levels? As in the K-12 system, there is consensus that “teacher quality,” defined broadly, plays a critical role in early childhood education. Whitebook, Gomby, Bellm, Sakai, and Kipnis (2009) argues that teachers, and the quality of interactions between teachers and children, are key determinants of preschool quality, and provides a summary of the extensive research base that supports this claim (Bowman, Donovan, & Burns, 2001; NICHD Early Child Care Research Network, 2000; Phillipsen, Burchinal, Howes, & Cryer, 1997; Vandell & Wolfe, 2000). However, there is substantial debate over the effect of specific degrees on teacher quality and student learning in early childhood settings. Some past research, including studies conducted in Head Start classrooms, report positive associations between teachers’ educational attainment and both observed care quality and

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child outcomes (Burchinal, Cryer, Clifford, & Howes, 2002; Helburn, 1995; Honig & Hirallal, 1998; Howes, Phillips, & Whitebook, 1992; NICHD Early Child Care Research Network, 1999; Zill et al., 2001). However, more recent research does not replicate this earlier pattern. This work has led to more nuanced hypotheses about the importance of pre-service teacher education (Early et al., 2006; Mashburn et al., 2008). To date, Early et al. (2007) provide the most methodologically rigorous analysis of this topic through their coordinated, secondary analysis of seven large, longitudinal datasets. Their findings, which accounted for the nested structure of the data as well as missing data, found little evidence of an association between teachers’ educational attainment or major and their students’ learning. These authors as well as other researchers point out, the null findings raise a number of questions and suggest the need for a broader research agenda to unpack the mechanism by which degree acquisition may improve quality in early childhood settings (Washington, 2008; Whitebook et al., 2009; Zaslow, Tout, Halle, Whittaker, & Lavelle, 2010). For instance, it may be that the effects of degrees vary depending on the specific quality, content, and structure of the degree program (Hyson, Tomlinson, & Morris, 2009). Perhaps the benefits from degree requirements may come indirectly through their impact on higher wages, professionalization, and reduced turnover (Whitebook & Ryan, 2011). Another possibility is that the impact of teachers’ degrees on program quality is moderated by other programmatic features, making it worthwhile to explore whether programs that raise teacher education levels make changes in other important program characteristics. This is the question this paper addresses.

2. Raising teacher education in Head Start Head Start is a federally funded preschool program serving over 900,000 low-income children through nearly 50,000 classrooms (Office of Head Start, 2010). Its mission is to promote school readiness broadly defined by providing educational, health, nutritional, and other services to children and their families. In 1998, Congress reauthorized Head Start with the Community Opportunities, Accountability, and Training and Educational Services (COATES) Act. The act mandated that 50% of all Head Start teachers nationwide have, at minimum, an AA degree in early childhood education or a related field by 2003. At that time, Head Start’s requirement for teachers was that they hold a Child Development Associate credential which is attained by clocking 120 h of formal early childhood education training, passing a written and oral assessment, and meeting several other requirements (Council for Professional Recognition, 2012). Many Head Start advocates worried the mandate was not accompanied by sufficient increases in funding (Barnett, 2003; Shaul, 2003). The legislation did require that in each year that the real value of Head Start funding appropriation increased, a set proportion of this increase would be used for quality improvements. Further, at least half of these quality improvement funds were to be used specifically for salary and benefit increases (Hart & Schumacher, 2005). In fact, between 1999 and 2001, the overall funding for Head Start increased, which translated to yearly increases in quality improvement funding. However, in 2001, funding peaked at $356 million and in the following years the much smaller gains in Head Start appropriations as well as the lower proportion set aside for quality improvements led to sharp drops in quality improvement funding ($80 million in 2002, and $32 million in 2003). Requirements to raise teacher education levels in this context of constrained resources may have led some Head Start programs to consider changes in other program areas.

Fig. 1. Shows the overall percentage of Head Start lead teachers nationwide who have acquired certain degrees (author’s calculation using annual Program Information Reports). Educational attainment for adults 25–29 comes from the Current Population Survey (CPS), March and Annual Social and Economic Supplement, 1971–2006.

Head Start was once again reauthorized in 2007 with the “Improving Head Start for School Readiness Act” which required that 50% of Head Start teachers nationwide hold a BA degree by 2013. According to one analysis, the estimated costs of meeting the goal would be approximately $2.7 billion over six years (Ewen, 2005). As with the prior reauthorization, the education demands were criticized for being “unfunded mandates” (National Head Start Association, 2008). Nonetheless, the goals set out in each of the reauthorization acts were met (see Fig. 1). While in 1998, 34% of teachers held an AA degree or more, by 2002 the mandated threshold was exceeded with 52% of teachers meeting the requirement. By 2011, 85% of teachers had an AA degree or more, and further, 52% of Head Start teachers held a four-year college degree, up from just 23% in 1999. As shown in Fig. 1, Head Start’s increase in teacher education levels outpaced changes in the educational attainment of the U.S. Population age 25–29 nationwide which is somewhat puzzling, given the constrained budget environment. As discussed above, one hypothesis is that Head Start programs that saw increases in their teacher education levels did so by making programmatic changes. A survey of 477 Head Start program directors conducted in 2008, explored how programs were operating given more stringent quality requirements but no additional resources (Allen & Smith, 2008). The findings from that survey support the hypothesis that programs adjusted other aspects of their operations in order to meet requirements. For instance, 78% reported that they eliminated staff positions and reassigned job responsibilities, 23% reported increasing class sizes, and 36% reported reducing programs for families (e.g. GED assistance, career development). The purpose of this paper is to explore the relationship between increased education levels and programmatic changes more rigorously, leveraging within-program fixed-effects models. 3. The current paper This study adds to a small, related literature investigating whether efforts to improve quality in early childhood settings are associated with unintended consequences or programmatic changes. Blau (2007) measures the effects of tougher quality regulations on day care centers and finds that the costs associated with meeting these requirements are transferred over to the child care workers in the form of lower earnings. Hotz and Xiao (2011), show that while child care regulations do lead to improvements

D. Bassok / Early Childhood Research Quarterly 28 (2013) 831–842

in care quality, they also lead to closures of centers, particularly in communities serving low-income children. The current study contributes to this existing literature by considering this issue of unintended consequences within the context of Head Start, a large, public program. It does so over a period in which quality improvements were an explicit priority and budgets were constrained. In addition, the study leverages a rich, longitudinal dataset covering programs nationwide and includes more detailed information about staff and service provision than is typically available. I first ask whether programs that experienced gains in the education levels of their teachers made changes with respect to program scope. As discussed above, the mission of Head Start is to promote school readiness through the provision of comprehensive services including education, health, nutrition and family support services. Indeed, the program is often distinguished from other public, early childhood education programs for its broader scope (Gormley, Phillips, Adelstein, & Shaw, 2010). I hypothesize that given limited resources and a requirement to raise teacher education, programs may reduce service provision. I use six measures of health and social service provision and examine whether increases in the education levels of Head Start teachers are associated with reductions in the provision of these services. Recent research shows that the Head Start program had long-term impacts on non-educational outcomes such as health and mortality, and suggests that the elimination of such services may have negative implications for children (Deming, 2009; Ludwig & Miller, 2007). Next, I explore whether Head Start programs that increased teacher education levels made changes with respect to other aspects of their staffing, such as the number of teachers they employ or the percentage of staff members that is classified as a teacher. Note that Head Start programs could increase the percentage of teachers with degrees in a number of ways, for instance, by incentivizing or aiding existing teachers to acquire the necessary degree or by replacing existing teachers with teachers who already possess the degree. They could also increase the percentage of teachers with a BA simply by eliminating positions held by teachers without a BA or by reclassifying these teachers into other (non-teacher) roles. The data used for this study does not allow me to track individual teachers over time to parse out the exact mechanism by which Head Start programs changed the composition of their teaching staff. However, I do observe a number of related program-level characteristics including the number of teachers employed, the child–teacher ratio, the rate of teacher turnover, and the percentage of the staff classified as teachers. If programs raised teacher education levels by firing or reclassifying teachers who lacked the desired degree, we might expect to see a drop in the number of teachers, or a shifting of the staff toward non-teaching positions. The extent to which Head Start programs could pursue such a strategy is constrained because Head Start programs regulate group size and child–teacher ratios. For instance, Head Start allows no more than 20 children in a classroom and specifies each classroom must be staffed by a teacher and aide or two teachers. Nevertheless, the regulations do leave some room for these types of staffing changes, which are worth examining due to their hypothesized link to child outcomes. Child–teacher ratios are a commonly regulated structural feature of child care settings, and a number of advocacy organizations recommend low ratios as a way to ensure quality interactions within classrooms (Barnett et al., 2011). These regulations are supported by research showing child–adult ratios in early childhood settings are associated with observed classroom quality, with children’s learning gains, and with longer-term outcomes like the likelihood of attending college (Burchinal et al., 2000; Chetty et al., 2011; NICHD Early Child Care Research Network, 2002). Similarly, several recent studies show that teacher instability negatively impacts children’s learning both in early childhood and in K-12

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settings (Ronfeldt, Lankford, Loeb, & Wyckoff, 2011; Tran & Winsler, 2011). Finally, I explore whether raising the educational requirements of Head Start teachers had the unintended consequence of changing the racial composition of the teacher labor force and whether it is related to a drop in the employment of current or former Head Start parents. Fuller, Livas, and Bridges (2005) estimate that 47% of California’s center-based care staff were non-Latino White in contrast to California’s K-12 system where 74% of all teachers were non-Latino White (Fuller et al., 2005). If this difference is driven by stricter education requirements in the public school system, increases in the requirements for Head Start teachers may impact the racial composition of their teaching force. This is because Black and Latino child care workers, on average, have lower levels of education and are less likely to enroll in degree programs relative to their White counterparts (Ackerman, 2005; Early & Winton, 2001; Maxwell, Lim, & Early, 2006). Black and Latino individuals are also particularly under-represented among four-year Early Childhood degree completers. For instance, the author’s calculations using the Integrated Postsecondary Education Data System show that in 1999, the year that the current analysis begins, 79% of Early Childhood BA completers were white, 8% were Black and 7% were Hispanic. In contrast, among AA completers these figures were 66%, 15% and 11%, respectively. The figures suggest that efforts to either raise existing teachers’ education levels or recruit new teachers with degrees may lead, unintentionally, to a whitening of the Head Start teaching force, particularly as programs raise the percentage of BA-level teachers. A shift in the racial and ethnic composition of the Head Start staff may have important implications if a shared racial or ethnic background between students and teachers impacts students’ learning. The evidence on the importance of racial congruence for children’s learning in early childhood settings is mixed. A number of studies indicate that early childhood teachers rate their relationships with students more positively when they share the child’s ethnicity, and that teachers also form more attached relationships with children who match their ethnicity (Howes & Shivers, 2006; Murray, Murray, & Waas, 2008; Saft & Pianta, 2001). However, using data from two large studies of early childhood care, Burchinal and Cryer (2003), found no evidence that child–teacher ethnic match was associated with child outcomes, a finding echoed in other studies (Ewing & Taylor, 2009). As a final measure, I consider the percentage of current and former Head Start parents employed on the staff. Head Start programs have a long history of involving parents in program operations and the program’s performance standards require all Head Start programs provide parents with opportunities to become volunteers or staff at the program (Zigler, Styfco, & Gilman, 1993). To the extent that Head Start parents are relatively less likely to hold or attain a degree, efforts to raise teacher education may result in less parents on staff. Like racial and ethnic congruence, the percentage of Head Start parents on staff can be thought of as a crude proxy for shared beliefs, expectations, and environments (Shivers, Sanders, Wishard, & Howes, 2007). However, Barbarin, Downer, Odom, and Head (2010) explicitly examine congruence between parents and teachers with respect to child-rearing beliefs and find that shared beliefs do not translate into more effective settings for young children.

4. Method 4.1. Participants Data for this analysis come from Program Information Reports (PIR), which are mandatory, program-level surveys collected from

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all Head Start programs annually. The terms “Head Start programs” and “programs” are used to refer to “Head Start delegates.” Head Start is made up of 10 regional offices and two offices that serve migrant populations and Native Americans. These offices award federal grants to public or private agencies (grantees) that can directly operate centers, or can pass some or all of their responsibility to delegate organizations which are also local agencies. In turn, delegates may operate any number of actual Head Start centers. The delegate-level administrative data utilized for this study are the finest-grained longitudinal data collected about Head Start programs nationwide. I constructed a 13-year panel covering the full “universe” of Head Start programs in each year between 1999 and 2011. The sample includes programs operating in the 50 U.S. states and Washington D.C. Programs that did not serve any children through center-based care (i.e. those that provided only homebased care), as well as those who had no funded enrollment or no teachers on staff were eliminated. The analytic sample includes 2283 programs and 23,832 program-by-year observations. Of those programs that remain in the sample, 64% appeared in all thirteen years, and about 93% are in the data for at least two years. Sample sizes for the analyses presented range from 12,700 to 23,800 primarily because some outcome variables are not available in every year of the panel but also due to a very limited number of observations with missing data. 4.2. Measures 4.2.1. Teacher education PIR provide information on the number of teachers with specific degrees (AA, BA, graduate) in each year. I use AA and BA to refer to Associate and Baccalaureate degrees which have many different designations (e.g. AA, AAT, BA, BS, B.Ed). Prior to 1999, data on the percentage of teachers with a BA or above is unavailable as are data on many of the study’s key outcome variables. For this reason, the current analysis begins in 1999. I constructed a variable measuring the percentage of teachers with an AA or above as well as two variables decomposing this group into the percentage with an AA and the percentage with a BA or above. 4.2.2. Health and social service provision PIR include a detailed section reporting the services Head Start children and their families receive. In the current analysis, I considered six outcomes, three that are child-focused and three that are family focused. Although there are many additional items in the PIR measuring child-directed services (e.g. the number of children up to date on immunizations or the number of children receiving preventative dental care), I focus on services directly provided by Head Start programs. Specifically, the child-directed measures include: (1) the percentage of newly-enrolled Head Start children who have completed a routine screening for developmental, behavior, and sensory concerns; (2) the percentage who have a disability defined as the percentage of children with an Individualized Education Program (IEP) indicating they have been determined eligible by the LEA to receive special education and related services; and (3) the average hours per month a mental health professional spends on site. The PIR also include 15 items about an array of services directed toward Head Start families. Programs were asked to report the number of families who received each of these 15 services since last year’s PIR, and separately to report the number of families that received at least one of the services. Services include the percentage of families who received English as a second language (ESL) courses, the percentage who received emergency or crisis intervention and the percentage who received services for substance abuse, domestic violence, or child abuse. The Cronbach’s alpha for these 15 items is 0.87. I consider as outcomes: (4) the percentage of Head Start

families that received at least one of the family services; (5) the percentage of families that received “parenting education,” which was, throughout the period considered, the most common family-directed service; and (6) a dichotomous variable measuring whether or not the Head Start program offers programs for fathers and father figures. Data on the enrollment rates of disabled children are available for the full panel. Information about mental health services, parenting education, and programs for fathers are available for 2002–2011, for developmental screenings data is available from 2002 to 2010 and for participation in at least one family service from 2005 to 2011. For all these variables I use the maximum available years of data. 4.2.3. Staffing The first three staffing measures are the number of teachers, the total enrollment, and the child–teacher ratio. The number of children enrolled is defined as all children who have been enrolled in the program and have attended at least one class or, for programs with home-based, options, received at least one home visit. Children in home-based or family child care are included in the total enrollment measure but not the child–teacher ratio. Two additional staffing outcomes are considered. First, I constructed a variable measuring the percentage of Head Start staff that is classified as a teacher, defined as the total number of teachers divided by the total number of staff members. This ratio would decrease either if teaching positions were fully eliminated or if teaching positions were converted into other types of staff roles. Second, I defined turnover as the number of teachers who left the program over the past year divided by the total number of teachers. Turnover data are only available from 2002 onwards. All other staffing variables are available for the full panel. 4.2.4. Racial composition PIR include items that measure the ethnic and racial composition of Head Starts’ enrollees and their child development staff. Using these data, I examine whether changes in the education levels of Head Start teachers over time are associated with changes in both the composition of the staff and the match between the racial background of the children and that of the staff. Each program is first asked to report the number of Head Start enrollees (or child development staff members) who are of Hispanic or Latino origin, and then asked separately about their racial composition. Respondents are instructed to report each individual in both an ethnicity category and a race category. I constructed three variables that measure the percentage of the child development staff that is classified as Hispanic, Black and White, as well as analogous measures for Head Start enrollees. Unfortunately, classroom-level data are not available, so racial divergence is defined crudely as the difference between the percentage of staff members and enrollees who are White at the program level. Prior to 2005, racial composition data were collected using a single item that asked about both race and ethnicity. This change in item wording makes direct comparisons before and after 2005 impossible so I limit the analysis of racial composition to seven years (2005–2011). A notable data limitation is that PIR ask for racial breakdowns for all child development staff combined which includes teachers, assistant teachers, home visitors, and family child care teachers. Because the mandates for increased educational attainment have primarily been targeted toward teachers specifically, it would be ideal to have data on the demographic characteristics broken down by job category. Finally, I construct the percentage of Head Start staff that are current of former Head Start parents. Note that while the race variables refer to the child development staff, the percentage of the staff who is a current or former Head Start parents refers to staff more broadly including all administrative, managerial, child

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development, content area, and support staff (such as custodians, bus drivers, etc.). 4.2.5. Covariates All models include time-varying controls for program characteristics that may be related both to the education levels of teachers and to the outcomes considered. For instance, Head Start’s program performance standards often mandate services only for children who have been in the program more than 45 days. However, PIR figures about service refer to all children enrolled in the past year, irrespective of the length of enrollment. The percentage of children that were in class for less than 45 days is likely to be correlated with a program’s service provision levels, and may also be related to their ability to recruit and retain degreed teachers. Therefore, all models in the paper control for this variable. Other time-varying measures include program size, racial composition of the enrolled children, percentage of parents that are unemployed, percentage of parents enrolled in school, and percentage receiving public aide (TANF, WIC), each included due to its possible correlation both with the outcome and the explanatory variables. Not all time-varying covariates are available for the full panel of data. In each model I maximize the number of covariates available for the years in which the key outcome and explanatory variable are available. 4.3. Analytic strategy This study used within-program, fixed-effects models to examine associations between changes in teacher education levels over time and changes in three program characteristics (service provision, staffing, and staff racial composition). Ideally, the study could address the question: did increases in teacher education cause Head Start programs to make changes in these other areas. However, in the absence of experimental data, it is difficult to identify a causal relationship. This is because the education levels of teachers in Head Start programs, or changes in their education over time, may be non-random. Fixed-effects models leverage the longitudinal nature of the Head Start data, and substantially reduce the risk of omitted variable bias relative to cross-sectional approaches. A cross-sectional analysis measures “level” associations controlling for observable covariates: For instance, do Head Start programs with relatively high teacher education levels provide different levels of family services relative to those with lower education levels? These types of models have very limited causal warrant because unobserved factors may be driving any observed relationship. In contrast, fixed-effects models rely on repeated measures of outcomes and explanatory variables within programs over time, and thus measure associations between changes rather than levels. A key advantage of these models is that they can account for all unobserved, time-invariant, factors. For instance, the model accounts for any time-invariant characteristics of the surrounding labor force or time-invariant preferences held within the program. The model estimated takes the form: Yit = ˇ0 + ˇ1 Teacher Educit + ˇ2 Program Charsit + ˇ4 Yeart + Fixed Program Effecti + εit Here Yit is a continuous measure of some characteristic of Head Start program i at time t, such as the percentage of Head Start children that receive a developmental screening or the percentage of the Head Start staff that is Hispanic. Teacher Educit is the key explanatory variable of interest, the percentage of the teachers at program i who hold a particular degree (e.g. an AA or above) in year t. I also control for a set of time-varying program characteristics describing the Head Start programs (e.g. enrollment, demographic

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characteristics). The model includes a set of “year” dummies (omitting the initial year) which account for secular trends over time in the outcome variable. The fixed program effect captures all timeinvariant factors that affect the outcome and εit is the residual error. The standard errors in this model are adjusted for clustering at the program level across years. The key coefficient of interest is ˇ1 which measures the association between the education levels of Head Start teachers and the various Head Start program characteristics. Note that time-invariant covariates such as the region or physical setting in which the program operates are excluded as they are “fixed” within units. I present two models for each outcome. In one, the key explanatory variable of interest is the percentage of teachers with an AA or above. In the other, this measure is disaggregated into two subcomponents: the percentage with an AA and the percentage of teachers with a BA or above. Presenting the data this way allows me to first measure the overall association between degree attainment and program outcomes, and then examine whether evidence of programmatic changes differed by degree type. In general, recruiting and retaining teachers with a 4-year degree is hypothesized to be more costly for programs and therefore yield greater associations with programmatic changes. 5. Results Results are presented in four sections. First, descriptive trends are discussed to highlight the change in teacher education levels over time and to describe trends in the outcome variables. The remaining three sections explore whether these changes in teacher education levels are associated with changes in: (1) service provision, (2) staffing decisions, and (3) the racial composition of the child development staff. 5.1. Descriptive trends Table 1 provides program-level descriptive statistics for teacher education levels as well as other program characteristics. During the 13 years covered by this panel, the percentage of teachers with an AA or above nearly doubled from 46% in 1999 to 86% in 2011. These figures are averages of program-level data and are therefore slightly different from the data presented in Fig. 1 which shows the percentage of teachers at the national level who have acquired certain degrees. A substantial portion of this change was driven by increases in the percentage of teachers with a BA or above which also rose, from 30% to 55% over the same period. The next panel of Table 1 highlights trends in service provision. In all but one case, an increase in service provision is evident. Most Head Start children are screened for developmental and behavioral issues, with the percentage rising from 86% in 2002 to 90 in 2010 (Comparable data on screenings are not available in the 2011 PIR). These high rates are perhaps not surprising given that Head Start programs are required to screen all children within 45 days of enrollment. Head Start performance standards also mandate that at least 10% of each program’s total enrollment be made available to children with disabilities. Programs exceed this requirement with disabled children making up approximately 13 or 14% of enrolled children in each year of the panel. There has been a marked increase in the percentage of Head Start families that received parenting education up to 54% from 33 in 2002. The third panel shows that both the number of teachers employed at Head Start programs and program enrollment have increased over time. That said, the child–teacher ratio has gradually dropped from 20 to 18. Turnover rates have varied over time, with the highest observed turnover in 2002, and the lowest in 2010 (17% versus 12%).

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Table 1 Descriptive statistics, program-level trends in key explanatory and outcome variables.

Number of programs Teacher education Teach AA+ Teach AA Teach BA+

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

1859

1832

1846

1874

1882

1879

1854

1864

1819

1786

1778

1787

1772

45.6 (34.1) 16.0 (21.5) 29.6 (32.1)

45.4 (32.4) 17.4 (21.7) 28.0 (31.5)

48.3 (31.4) 19.4 (21.6) 28.9 (31.5)

57.7 (30.4) 23.4 (23.0) 34.3 (32.5)

63.0 (28.9) 27.2 (24.5) 35.9 (32.6)

70.7 (26.5) 31.3 (26.0) 39.4 (32.5)

75.0 (24.6) 33.8 (26.9) 41.3 (32.7)

77.6 (23.6) 33.9 (27.0) 43.7 (32.8)

79.8 (23.0) 34.2 (27.2) 45.6 (32.7)

79.8 (23.1) 33.8 (26.7) 46.0 (32.3)

82.4 (22.5) 33.5 (26.6) 48.9 (32.2)

83.6 (21.7) 32.3 (25.9) 51.3 (31.3)

86.0 (20.0) 31.4 (25.2) 54.6 (30.7)

13.8 (7.3)

86.0 (25.5) 13.3 (7.4) 48.5 (128.1)

85.9 (25.8) 13.3 (7.0) 48.2 (127.5)

85.6 (26.5) 13.5 (7.5) 50.0 (94.8)

33.3 (34.5) 74.4 (43.7)

34.0 (34.2) 80.9 (39.3)

34.7 (34.6) 81.6 (38.8)

87.4 (24.8) 13.6 (7.8) 48.7 (92.9) 66.8 (29.0) 38.6 (35.9) 85.8 (35.0)

88.9 (21.7) 13.1 (7.3) 48.7 (93.7) 70.6 (29.0) 42.5 (36.9) 87.1 (33.5)

89.9 (20.0) 13.2 (7.2) 51.8 (94.7) 71.4 (29.3) 43.5 (37.6) 87.5 (33.1)

89.3 (19.8) 13.2 (7.3) 54.7 (127.6) 74.0 (27.8) 46.8 (37.3) 87.1 (33.6)

90.8 (14.7) 12.9 (7.5) 51.8 (107.2) 74.9 (28.7) 49.3 (38.0) 87.6 (32.9)

89.8 (15.6) 12.6 (7.1) 51.1 (106.7) 76.5 (28.1) 52.1 (37.7) 88.3 (32.1)

12.8 (7.0) 52.3 (123.6) 78.7 (26.9) 53.9 (37.7) 88.4 (32.0)

23.7 (31.8) 479.3 (637.5) 18.4 (6.7) 23.9 (9.1) 16.8 (19.9)

25.0 (34.2) 494.6 (661.6) 17.8 (6.2) 24.3 (9.0) 14.7 (17.9)

24.8 (33.1) 497.5 (664.1) 17.8 (6.6) 24.3 (9.0) 15.1 (18.3)

25.0 (34.8) 502.0 (665.1) 17.6 (6.1) 24.0 (8.7) 16.1 (18.4)

25.4 (36.7) 510.0 (683.2) 17.4 (6.1) 23.8 (8.6) 15.9 (18.0)

25.5 (37.8) 511.9 (685.8) 17.5 (6.0) 24.2 (8.8) 15.9 (17.9)

26.1 (38.9) 522.1 (703.5) 17.4 (5.5) 24.3 (8.7) 16.6 (17.7)

26.0 (39.1) 518.4 (682.9) 17.5 (5.5) 24.2 (8.6) 13.5 (15.9)

27.0 (40.4) 527.0 (699.5) 17.5 (5.5) 24.6 (9.0) 11.9 (14.7)

27.1 (41.3) 527.6 (699.9) 17.6 (5.5) 24.4 (8.7) 13.2 (15.7)

51.8 (37.4) 40.6 (35.1) 11.2 (21.5)

53.7 (37.6) 43.5 (35.6) 10.2 (22.1)

54.3 (37.7) 43.2 (35.3) 11.2 (21.0)

53.9 (38.0) 43.5 (35.4) 10.4 (21.1)

56.2 (37.4) 44.3 (35.4) 11.9 (20.0)

56.3 (37.4) 44.8 (35.3) 11.5 (21.0)

57.2 (36.9) 45.4 (35.3) 11.9 (20.0)

24.6 (31.7) 25.8 (31.2) -1.2 (12.4)

24.3 (31.7) 25.3 (31.0) -1.0 (12.3)

23.9 (31.5) 24.8 (30.6) -0.9 (13.3)

23.4 (31.3) 24.4 (30.5) -0.9 (12.7)

23.4 (31.4) 24.4 (30.4) -1.0 (12.4)

23.1 (31.1) 23.9 (29.8) -0.8 (12.3)

22.6 (30.6) 23.4 (29.5) -0.8 (12.6)

19.2 (27.4) 26.5 (30.9) −7.3 (16.8)

19.8 (27.9) 28.0 (31.2) −8.2 (18.1)

19.6 (27.8) 28.2 (31.1) −8.7 (17.9)

19.7 (27.8) 28.3 (31.0) −8.6 (16.6)

19.9 (27.7) 28.9 (31.2) −9.0 (17.4)

19.8 (27.4) 29.2 (31.1) −9.4 (16.2)

20.1 (27.3) 29.7 (31.2) −9.6 (16.0)

28.6 (18.3)

28.3 (18.5)

28.3 (17.8)

28.1 (17.8)

28.8 (18.2)

28.4 (17.7)

28.3 (17.7)

Child and family services Developmental screening Disabilities

14.0 (7.1)

13.7 (7.1)

Mental health hours Family services Parenting education Programs for fathers (binary) Staffing # of teachers Enrollment Child/tchr ratio % staff, teachers

20.9 (29.2) 442.3 (556.0) 19.8 (9.5) 25.6 (9.4)

21.0 (28.3) 452.8 (561.4) 19.7 (7.5) 25.1 (8.6)

22.3 (29.2) 462.6 (603.8) 18.9 (6.8) 24.6 (8.7)

Turnover Racial composition Staff, white Enrlmnt,white Difference (White staff − white enrollment) Staff, black Enrlmnt black Difference (Black staff − black enrollment) Staff hispanic Enrlmnt hispanic Difference (Hispanic staff − hispanic enrollment) % staff, parents

31.8 (19.1)

32.3 (18.8)

31.1 (18.6)

29.7 (18.3)

29.1 (19.0)

28.8 (18.4)

Notes: Program-level means, standard deviations in parentheses. Children in home-based or family child care are included in the total enrollment measure but not the child to teacher ratio.

The final panel shows that in 2005, roughly 41% of Head Start enrollees were White and that Black and Hispanic children comprised just over a quarter of enrollees each. Recall that race and ethnicity are separated on the PIRs such that the White and Black categories will include Hispanic individuals and vice versa. Between 2005 and 2011, the percentages of White children enrolled increased from 41 to 45%, as did the percentage of Hispanic students, from 27 to 30%. Over the same time period, the percentage of staff that is White also increased from 52 to 57%. The racial divergence variable, which measures the difference between the percentage of staff and the percentage of students that are White,

fluctuates between 10% and 12% points in every year, indicating that at the program level, White staff members are overrepresented relative to White enrollees in every year. The percentage of parents employed as staff members has gradually fallen from 32 to 28%. 5.2. Teacher education and service provision Table 2 shows results from 12 within-program, fixed effects models estimating whether changes in teacher education levels are related to changes in service provision. For each of six outcomes, I present results from two models. In the first, the explanatory

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Table 2 Within-program fixed effects models estimating changes in Head Start service provision. Developmental screening (1) Pr. Tchrs AA+

Disabilities (2)

(3)

Mental health professional (hours) (4)

−0.003 (0.003)

0.020 (0.015)

(5)

(6)

0.088* (0.040)

Pr. Tchrs AA

0.021 (0.017)

−0.005 (0.004)

0.012 (0.047)

Pr. Tchrs BA+

0.018 (0.018)

0.001 (0.005)

0.186** (0.064)

Observations R-squared Years

Pr. Tchrs AA+

16,522 0.268 2002–2010

16,522 0.268 2002–2010

18,289 0.702 2002–2011

Families receiving at least one service

Parent education

(7)

(9)

(8)

18,289 0.702 2002–2011

18,231 0.533 2002–2011

(10)

(11)

Programs for fathers

***

0.043 (0.023)

18,231 0.534 2002–2011

0.068 (0.020)

(12)

0.041 (0.023)

Pr. Tchrs AA

0.012 (0.028)

0.042 (0.023)

0.039 (0.026)

Pr. Tchrs BA+

0.076*** (0.026)

0.102*** (0.024)

0.043 (0.026)

Observations R-squared Years

12,655 0.581 2005–2011

12,655 0.581 2005–2011

18,289 0.560 2002–2011

18,289 0.560 2002–2011

18,287 0.420 2002–2011

18,287 0.420 2002–2011

Notes: Sample sizes vary across regressions due to changes in item availability in the PIR surveys. All models include program fixed effects as well as controls for available time-variant covariates as discussed in the paper. Robust standard errors in parentheses. * p < 0.05. ** p < 0.01. *** p < 0.001.

variable of interest is the percentage of teachers with an AA or above. The second model shows results when this variable is decomposed into the percentage with an AA and then the percentage with a BA or above. A negative coefficient on any of the teacher education variables indicates that programs in which the percentage of teachers with degrees rose also saw declines in service provision. None of the models suggest this relationship. For five of six outcomes, the coefficients are positive, and the negative coefficients in the model predicting enrollment of children with disabilities are practically small and statistically insignificant. Contrary to the hypothesized negative relationship, as the percentage of teachers with a BA increased, certain types of service provision rose as well. For instance, a 1 percentage point increase in teachers with a BA is associated with a 0.186 h (or roughly 11 min) increase in the reported time a mental health specialist is available within a center. Put another way, a standard deviation increase in the percentage of teachers with a BA, which is equivalent to an increase of about 32 percentage points, is associated with about six extra hours of mental health services per month (32 × 0.186), a meaningful increase over the baseline level of approximately 49 h. Similarly, a standard deviation increase in the percentage of teachers with a BA is associated with about a 3 percentage point increase in the percentage of families who receive at least one service and those who receive parental education. Overall then, this model suggests that changes in teacher education levels did not lead to reductions of child and family services. All models yielded null or positive findings and indicate that increases in teacher education – particularly teachers with a BA – are at times associated with increases in several types of service provision. 5.3. Teacher education and staffing Table 3 presents results from similar fixed-effects models examining the relationship between teacher education levels and various

measures of staffing. The top row indicates that programs that experienced increases in teacher education also saw statistically significant drops in the number of teachers employed, but no changes in enrollment levels. Models 5 and 6 show that taken together, these two trends imply a modest but statistically significant increase in the child–teacher ratio. A one-percentage-point increase in the percentage of teachers holding an AA or above amounts to a 0.02 percentage point increase in the ratio. This means that a standard deviation increase in teachers with AA or above, equivalent to an increase of about 34 percentage points, amounts to an increase of about one child (0.68) per teacher. A similar increase in teacher education levels is also associated with about a 1 percentage point drop (1.08) in the percentage of staff classified as teachers. The final two models indicate that increases in teacher education are also related to increases in turnover. A standard deviation increase in teachers with an AA or above is associated with about a 1 percentage point (0.85) increase in turnover, over a baseline level of about 17%. Model 10 shows this pattern is driven primary by the increases in teachers with a BA or above. Taken together, the results in Table 3 show that programs in which teacher education increased also saw increases in both child–teacher ratio and in teacher turnover. 5.4. Teacher education and racial composition of staff The top panel of Table 4 shows that increases in the percentage of teachers with a college degree are associated with a “whitening” of the child development staff. Models 2, 4 and 6, which disaggregate degree attainment show that this pattern is specifically driven by the increases in teachers with a BA or above, and not by increases in teachers with a two-year degree. A standard deviation increase in the percentage of teachers with a BA is associated with about a 2 percentage point increase in the percentage of staff classified as

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D. Bassok / Early Childhood Research Quarterly 28 (2013) 831–842

Table 3 Within-program fixed effects models estimating changes in Head Start staffing. Number of teachers (1) Pr. Tchrs AA+

Enrollment (2)

(3)

−0.023** (0.007)

Child/teacher ratio (4)

(5)

(6)

0.022*** (0.003)

0.125 (0.099)

Pr. Tchrs AA

−0.032*** (0.009)

0.091 (0.107)

0.026*** (0.003)

Pr. Tchrs BA+

−0.013 (0.008)

0.167 (0.111)

0.016*** (0.004)

Observations R-squared Years

23,825 0.936 1999–2011

23,825 0.936 1999–2011

23,825 0.965 1999–2011

23,825 0.965 1999–2011

Percent of staff who are teachers (7) Pr. Tchrs AA+

23,825 0.654 1999–2011

23,825 0.655 1999–2011

Turnover rates (8)

(9)

−0.032*** (0.004)

(10)

0.025* (0.012)

Pr. Tchrs AA

−0.033*** (0.004)

0.011 (0.014)

Pr. Tchrs BA+

−0.030*** (0.005)

0.043** (0.016)

−0.032*** Observations R-squared Years

23,825 0.599 1999–2011

23,825 0.599 1999–2011

18,289 0.359 2002–2011

18,289 0.359 2002–2011

Notes: Sample sizes vary across regressions due to changes in item availability in the PIR surveys. All models include program fixed effects as well as controls for available time-variant covariates as discussed in the paper. Robust standard errors in parentheses. * p < 0.05. ** p < 0.01. *** p < 0.001.

white, a 1 percentage point drop in the percentage of staff classified as Hispanic and a slightly smaller (0.67) drop in the percentage of staff that is Black. Similarly, the findings on racial divergence indicate that as programs increased the percentage of teachers with a degree, divergence, defined as the program-level difference in the percentage of staff members and enrollees that are white, broadened. In other words, the increase in white workers at these centers was not accompanied by a similar increase in white enrollees. The final models indicate that increases in teacher education were also accompanied by drops in the percentage of Head Start staff members who were current or former parents. Here too the relationship is driven by changes in the percentages of teachers with a BA. A standard deviation increase in the percentage of teachers with a BA is associated with about a 1.6 percentage point decrease in the percentage of staff that is Head Start parents. 6. Discussion Between 1998, when the Head Start reauthorization took place, and 2011, the percentage of teachers with an AA or above more than doubled. The percentage of teachers with a BA or above also rose substantially, and in 2011 it surpassed the 50% mark. This study used a rigorous, within-program fixed-effects model to examine whether programs that experienced changes in teacher education levels also experienced changes with respect to other programmatic characteristics including child and family service provision, staffing, and the racial and ethnic composition of the staff. Taken together, this study provides suggestive evidence that efforts to improve quality in Head Start programs through heightened education requirements for teachers may yield unintended consequences, a finding that is consistent with earlier research on the impacts of quality regulations in the child care industry (Blau, 2007; Hotz & Xiao, 2011). That said, for the most part, the observed trade-offs were modest in magnitude.

6.1. Service provision Encouragingly, the study shows that Head Start programs that raised their teachers’ education levels did so without sacrificing programmatic scope as measured by several service provision indicators. Head Start service provision actually increased over the study period. The study demonstrates that those programs that increased their teacher education levels did not experience a differential drop in health or social service provision. On the contrary, in some cases, increases in teacher education within programs were positively associated with service provision. For instance, programs that increased the percentage of teachers with a BA also saw increases in the number of hours a mental health professional was available and the percentage of families that experienced at least one support service. This paper hypothesized that given constrained budgets, some programs that raised their teachers’ educational attainment would do so through drops in service provision. There is no evidence of this pattern and instead the results raise the possibility that degreed teachers and service provision may act as complements. This unexpected result is heartening given recent quasi-experimental studies which provide compelling evidence that Head Start has substantial long-term impacts on a number of non-cognitive outcomes such as health and mortality (Gibbs, Ludwig, & Miller, 2011; Ludwig & Miller, 2007). For instance, Deming (2009) finds that as adults, Head Start participants are several percentage points less likely to be in poor health relative to their siblings who did not participate in the program. The comprehensive services the Head Start program provides to children and their families are plausible partial explanation for these effects. It may be that teachers with higher educational attainment are able to help families access these services. One caveat worth highlighting is that the available PIR items do not measure the intensity of service provision with much nuance. The survey indicates, for example, the number of Head Start

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Table 4 Within-program fixed effects models estimating changes in the racial/ethnic composition of Head Start child development staff. Hispanic staff

Black staff

(1) Pr. Tchrs AA+

(2)

(3)

−0.017 (0.011)

White staff (4)

(5)

−0.008 (0.006)

(6)

0.036** (0.013)

Pr. Tchrs AA

−0.002 (0.013)

0.004 (0.007)

0.014 (0.015)

Pr. Tchrs BA+

−0.031** (0.012)

−0.021** (0.008)

0.059*** (0.015)

Pr Enr hispanic

0.105** (0.036)

0.105** (0.036)

−0.005 (0.009)

−0.006 (0.009)

0.029 (0.019)

0.030 (0.020)

Pr Enr black

−0.037 (0.024)

−0.037 (0.024)

0.185*** (0.040)

0.185*** (0.040)

−0.271*** (0.039)

−0.271*** (0.039)

Observations R-squared Years

12,655 0.925 2005–2011

12,655 0.925 2005–2011

12,655 0.971 2005–2011

12,655 0.971 2005–2011

12,655 0.907 2005–2011

12,655 0.908 2005–2011

Staff/child racial divergence (7) Pr. Tchrs AA+

Percentage of parents on staff (8)

(9)

(10)

−0.020 (0.007)

*

**

0.038 (0.015)

Pr. Tchrs AA

0.026 (0.018)

−0.002 (0.008)

Pr. Tchrs BA+

0.051** (0.017)

−0.042*** (0.008)

Observations R-squared Years

12,655 0.562 2005–2011

12,655 0.563 2005–2011

23,825 0.721 1999–2011

23,825 0.722 1999–2011

Notes: Sample sizes vary across regressions due to changes in item availability in the PIR surveys. Staff racial composition refers to the child development staff, which includes teachers, assistant teachers, home visitors and family child care teachers. Racial divergence is defined as the difference between the percent of the staff and enrollees that are White. All models include program fixed effects as well as controls for available time-variant covariates as discussed in the paper. Robust standard errors in parentheses. * p < 0.05. ** p < 0.01. *** p < 0.001.

families who have participated in parental education programs, but does not measure the frequency, length, or quality of these sessions. If programs made changes along this “intensity” margin, the results of this study would not capture them. Still, the lack of an overall drop in service provision across a number of outcomes is encouraging. 6.2. Staffing On the other hand, the current results do suggest that programs that increased the percentage of teachers with degrees also saw small drops in the number of teachers employed, but no changes in the number of children enrolled. As a result, child–teacher ratios rose modestly; a standard deviation increase in the percentage of teachers with AA or above, which is equivalent to an increase of about 34 percentage points in teachers with degrees, was associated with an increase of less than one child (0.68 children) per teacher. Head Start performance standards clearly regulate group sizes and ratios allowing no more than 20 children in a classroom and specifying each classroom must be staffed by a teacher and aide or two teachers. These regulations limit the extent to which programs could eliminate teaching positions as a strategy for raising the percentage of teachers with a degree. Nevertheless, the modest but statistically significant negative relationship is notable due to the research evidence showing that class sizes and child–teacher ratios in preschool and kindergarten are related to a number of short and long-term child outcomes including high school completion (Burchinal et al., 2000; Chetty et al., 2011; NICHD Early Child Care Research Network, 2002). While the small

change observed in the current study likely had a modest impact for enrolled children, in contexts where ratios and group size are less strictly regulated, efforts to raise teacher degrees may have larger impacts. The results also show that increases in teacher education, particularly the percentage of teachers with a four-year degree are related to increases in turnover. This pattern is not surprising, as efforts to increase teacher education levels likely rely, in part, on replacing existing teachers with individuals who hold the required degree. By definition, this process will lead to some turnover. Research suggests that increases in turnover have negative implications for young children (Ronfeldt et al., 2011; Tran & Winsler, 2011). However, evidence from the existing turnover literature suggests that the initial turnover may be short-lived, if the degreed teachers who are placed in classrooms are more stable than those teachers they replace. For instance, Whitebook and Sakai (2003) found that highly trained early childhood teachers were less likely to turnover in settings in which a higher percentage of the teaching staff held a BA. Future research is warranted to investigate whether Head Start’s move toward more educated teachers ultimately results in lower rates of turnover and more stability for young children. 6.3. Staff racial and ethnic composition In the final analysis of the paper, I examine whether changes in the education levels of teachers are related to increases in the percentage of child development staff that is white, the racial divergence between children and their teachers, and the percentage of parents employed as staff. I find evidence of all three of these

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patterns. In most, though not all, cases, the change is driven by increases in the percentage of teachers with a BA or above, rather than those with a two-year degree. Again, this pattern is not surprising, given that white early childhood educators tend to have more years of education than their Black or Hispanic peers, and that white individuals are overrepresented among individuals who either enroll in or complete a four year degree program (Ackerman, 2005; Early & Winton, 2001; Maxwell et al., 2006). Nevertheless, this paper is the first to empirically document that moving toward a more degreed teaching staff is associated with a whitening of the teaching force, a concern that is voiced in the early childhood community. For instance, Washington (2008) describes a sense of ambivalence among the early childhood workforce about the heightened focus on degree requirements and indicates there is a perceived tension between degree acquisition and the fields’ commitment to diversity. Earlier research provides insights regarding the implications of these changes for children enrolled in Head Start. In particular, Burchinal and Cryer (2003) and others find no evidence that a racial or ethnic match is positively related to child outcomes in early childhood settings, and demonstrate that stimulating, sensitive teachers effectively teach children, irrespective of racial congruence (Ewing & Taylor, 2009). However, a number of studies do find that teachers rate their relationships with children more highly when they share racial or ethnic background (Howes & Shivers, 2006; Murray et al., 2008; Saft & Pianta, 2001), and suggest that these relationships are important in shaping children’s learning trajectories. Shivers et al. (2007) argue that racial and ethnic match are proxies for “cultural continuity” which they suggest meaningfully impacts children’s development. There is also evidence from the K-12 sector that racial match between children and teachers impacts children’s learning trajectories. It is important to note that the K-12 setting is distinct from the early childhood one in notable ways including the higher educational requirements for K-12 teachers. That said, Dee (2004) leverages random-assignment data from the Project Star class size experiment and finds that kindergarteners who are randomly assigned to an “own-race” teacher meaningfully outperform their peers with respect to math and reading achievement. Other studies tackled this question using datasets in which the same child is observed with two teachers, one who matches them racially and one who does not. These studies again show that school-aged children benefit when assigned to teachers who share their racial background (Dee, 2005; Hanushek, Kain, O’Brien, & Rivkin, 2005). Given the mixed evidence, further research on the impacts of the racial changes observed among Head Start’s staff on children’s learning is worth pursuing. Finally, the impact of the observed drop in parental employment on staff on child outcomes is ambiguous. No study that I am aware of has explored this issue. A decline in parental involvement on staff may lead to a loss of shared culture or belief congruence between staff and enrolled children and families. However, the existing empirical research suggests that belief congruence, at least with respect to child-rearing attitudes- is not related to child outcomes (Barbarin et al., 2010). It may be that drops in parental employment on staff may impact children indirectly, if the wages these positions provide are used, in part, to support children. Unfortunately, in the current study I have no way of testing whether drops in parental employment were related to changes in families’ income. More research is necessary to understand the impact of each of the programmatic changes observed on child outcomes, and to assess how any potential costs or benefits compare to the benefits of increases in degreed teachers. That said, the current study provides a novel, preliminary look at the effects of a

teacher education mandate on program characteristics in Head Start. 7. Limitations While this paper adds to our understanding of programmatic changes associated with increasing teacher education levels in early childhood settings, the PIR data have several important limitations detailed below. As discussed above, the PIR data provide a long, nationally-representative, program-level dataset with which to study how early childhood programs change over time. However, the data is self-reported by programs, and the accuracy of the reports is not verified (Levinson, 2007; Government Accountability Office, 2008). If programs tend to report overly optimistic information, this would bias the results of the current study, particularly if the likelihood of misreporting has changed over time. In addition, some of the items are relatively crude and do not provide the desired specificity. For instance, the data on racial match is available at the program rather than classroom level, and therefore serves only as a proxy for the racial match children actually experience. Similarly, information about race and parental involvement is reported for the child development staff and for the full staff respectively. Ideally, this information would be available specifically for teachers, since they are the group of workers who were targeted with the education mandates. Despite these limitations, the analysis expands our understanding of efforts to change the early childhood labor force, as detailed below. 8. Conclusions & directions for future research The current study provides the first large-scale, empirical examination of potential program-level trade-offs associated with efforts to raise teacher education in Head Start. The findings from this paper provide evidence of changes both with respect to staffing patterns (child–teacher ratios, turnover) and with respect to the staff’s racial composition, though notably not with respect to service provision. In many cases, changes were primarily related to increases in the percentage of teachers with four rather than two year degrees. Recruiting teachers with a BA or supporting current teachers as they pursue this degree seems to pose a greater challenge for Head Start programs, a finding that is not surprising given the more limited access to four year degree programs in early childhood education or related topics (Bassok, 2010). While the analysis focused specifically on the Head Start program, some of the overarching findings may inform a broader conversation. In the 2013, President Obama released his plan for a preschool expansion and called for, “well-trained teachers, who are paid comparably to K-12 staff” (White House, Office of the Press Secretary, 2013). The current study suggests that in thinking through efforts to substantially change the composition of the early childhood labor force, it may be important to consider both direct and indirect effects, including the potential for unintended consequences. The study is a first step toward understanding how policy initiatives requiring higher education for early childhood educators may impact programs and in turn children. Moving forward, additional research is essential to assess how the changes that occurred in Head Start programs, both with respect to higher levels of teacher education and with respect to the related staffing changes, impacted the quality of experiences young children experienced and the overall effectiveness of the program. The composition of the teaching force at Head Start has changed substantially over the past 15 years, and studying the effects of these changes on children will provide important lessons. Such analysis would also add to the existing literature on the effects of teacher education in early childhood settings which to date has largely leveraged cross-sectional variation in teacher education.

D. Bassok / Early Childhood Research Quarterly 28 (2013) 831–842

The main hurdle for pursuing this work is the lack of highquality data longitudinally tracking both the early childhood labor force and child outcomes. For instance, no large-scale dataset exists tracking early childhood teacher career trajectories and the development trajectories of the children they teach. In recent years, there have been proactive efforts to improve data availability (Committee on Early Childhood Care & Education Workforce, 2012; Government Accountability Office, 2012). In the meantime, however, researchers and policy makers must make due with imperfect data. The PIR, even with their limitations, allow us a new window into a timely research question that cannot be addressed with other existing datasets about programmatic changes that occurred as Head Start raised teacher education. Though not longitudinal, existing data such as the Head Start Family and Child Experiences Survey, which includes five repeated cross-sectional analyses of Head Start children and programs from 1999 to 2009, may provide some insights about the relationship between changes in teacher characteristics and child outcomes, particularly if these data could be linked to the rich PIR data. In addition, more qualitative work including structured interviews with program directors could help validate and contextualize the findings of the current study. This type of work would help answer some of the process questions that the current study leaves unanswered about the specific strategies used to raise teacher education, the roadblocks encountered, etc. The current study demonstrates that efforts to raise the educational attainment of Head Start teachers are associated with changes in other programmatic features. The results are policyrelevant both because of the potential for direct implications from these changes for children and because they raise the possibility that the unintended consequences associated with efforts to raise teacher attainment at Head Start may moderate the relationship between teacher degrees and child outcomes. Future efforts to measure the benefits of policies aimed at a more educated early childhood labor force should account for the possible costs associated with recruiting and retaining teachers with degrees. References Ackerman, D. J. (2005). Getting teachers from here to there: Examining issues related to an early care and education teacher policy? Early Childhood Research & Practice, 7(1), 1–8. Allen, B., & Smith, A. (2008). Reduced funding cripples Head Start from reaching its potential. Alexandria, VA: National Head Start Association. Barbarin, O. A., Downer, J., Odom, E., & Head, D. (2010). Home-school differences in beliefs, support, and control during public pre-kindergarten and their link to children’s kindergarten readiness. Early Childhood Research Quarterly, 25(3), 358–372. Barnett, W. S. (2003). Low wages = low quality: Solving the real preschool teacher crisis [Preschool Policy Matters No. 3]. New Brunswick, NJ: National Institute for Early Education Research, Rutgers University. Barnett, W. S., Carolan, M. E., Fitzgerald, J., & Squires, J. (2011). The state of preschool: 2011 state preschool yearbook. New Brunswick, NJ: National Institute for Early Education Research, Rutgers University. Bassok, D. (2010). Three essays on early childhood education policy. Stanford, CA: Graduate School of Education, Stanford University (Unpublished doctoral dissertation). Blau, D. M. (2007). Unintended consequences of child care regulations. Labour Economics, 14(3), 513–538. Bowman, B. T., Donovan, S., & Burns, M. S. (2001). Eager to learn: Educating our preschoolers. Washington, DC: National Academy Press. Burchinal, M. R., & Cryer, D. (2003). Diversity, child care quality, and developmental outcomes. Early Childhood Research Quarterly, 18(4), 401–426. Burchinal, M. R., Cryer, D., Clifford, R. M., & Howes, C. (2002). Caregiver training and classroom quality in child care centers. Applied Developmental Science, 6(1), 2–11. Burchinal, M. R., Roberts, J. E., Riggins, R., Jr., Zeisel, S. A., Neebe, E., & Bryant, D. (2000). Relating quality of center-based child care to early cognitive and language development longitudinally. Child Development, 71(2), 339–357. Chetty, R., Friedman, J. N., Hilger, N., Saez, E., Schanzenbach, D. W., & Yagan, D. (2011). How does your kindergarten classroom affect your earnings? Evidence from Project Star. The Quarterly Journal of Economics, 126(4), 1593–1660.

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