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Amy AulettoMichigan State University

Joshua CowenMichigan State University

White Paper No. 2June 21, 2018

Teacher Training, Teacher Placement, and Teacher Mobility: Evidence from Michigan 2011–2015

Abstract

The role of teacher preparation in improving student access to high quality teaching, particularly for those in high-need schools, is of increasing interest in policy, practice, and research. In this study, we use administrative data from Michigan to observe relationships between teacher education programs and teachers’ employment outcomes. We describe a systematic approach for characterizing teacher preparation in Michigan and organize teacher education programs into four unique clusters. We also observe a number of institutional characteristics that predict the types of schools teachers enter as well as exit and transfer rates from those schools. We find that graduates of academically rigorous institutions are less likely to work in high-need schools and more likely to exit the profession. We find higher rates of exit and transfer associated with more racially diverse programs. Our results have implications for teacher preparation, particularly for high-performing programs and those preparing teachers of color.

Education Policy Innovation Collaborative——————————————— College of Education Michigan State University

� Education Policy Innovation Collaborative

›

EPIC working papers are designed to provide timely analyses of current policy questions. They have not undergone final peer review and should not be cited or distributed without permission from the authors. EPIC working papers are carefully vetted and prepared prior to distribution in this series, but we reserve the right to amend these reports as part of the final process of publication over time.

Funding for this paper was provided in part by the Laura and John Arnold Foundation and an anonymous foundation. For data and policy-related advice we thank Erika Bolig and Venessa Keesler of the Michigan Department of Education, and Mel Bisson and Thomas Howell from the Michigan Center for Educational Performance and Information. We also thank Paul Bruno, Dongsook Han, Tara Kilbride, Brad Marianno, Jesse Nagel and Kelly Stec for excellent research assistance related to this project, and Ken Frank, Dan Goldhaber, and participants in the New York Federal Reserve Board seminar series for comments and suggestions. All errors are our own.

DISCLAIMER: This research result used data collected and maintained by the Michigan Department of Education (MDE) and/or Michigan’s Center for Educational Performance and Information (CEPI). Results, information and opinions solely represent the analysis, information and opinions of the author(s) and are not endorsed by, or reflect the views or positions of, grantors, MDE and CEPI or any employee thereof.

�Teacher Training, Teacher Placement, and Teacher Mobility �2

Education Policy Innovation Collaborative——————————————— College of Education Michigan State University


I. Introduction Previous research has demonstrated that teachers are the most important school-based

determinant of student outcomes (see Aaronson, Barrow, & Sander, 2007; Chetty, Friedman, & Rockoff, 2014a, 2014b; Hanushek, 2011; Konstantopoulos, 2009; Nye, Konstantopoulos, & Hedges, 2004; Rivkin, Hanushek, & Kain, 2005; Rockoff, 2003; Sanders & Rivers, 1996). The benefits of having an effective teacher include both academic achievement outcomes and other positive effects that can be detected well into adulthood. Yet despite the importance of teachers, students do not have equal access to this critical educational resource. Low-income students and students of color, as well as those with a history of lower academic performance, are more likely to be taught by less effective teachers (Cowan & Goldhaber, 2015; Dieterle, Guarino, Reckase, & Wooldridge, 2015; Glazerman, Max, Teh, & Protik, 2011; Shultz, 2014). These marginalized student populations are also more likely to attend schools disproportionately impacted by teacher turnover (Feng, 2009; Goldhaber, Gross, & Player, 2011; Hanushek, Rivkin, & Schman, 2016). These higher rates of teacher churn are disruptive to schools regardless of the effectiveness of the teachers who leave (Ronfeldt, Loeb, & Wyckoff, 2013) and teacher turnover negatively impact schools through loss of productivity and experience level (Hanushek et al., 2016).

Considering the role of teacher education programs (TEPs) in either contributing to, or perhaps mediating, these patterns among their graduates has been one recent approach to addressing inequitable teacher distribution and mobility patterns (Lubell & Putnam, 2016; U.S. Department of Education, 2014). Teachers who participate in traditional preparation routes spend years completing coursework and clinical experiences prior to entering the classroom. It follows then that teachers’ employment outcomes, including the types of schools they work in, how long they stay in the profession, and their transfer behaviors, are likely influenced to some extent by the post-secondary preparation they received before beginning their careers. There is a growing body of research demonstrating the relationship between TEPs and teacher mobility (Goldhaber & Cowan, 2014) as well as a number of other measures of teacher effectiveness (Darling-Hammond; Holtz, Gatlin, & Heilig, 2005; Goldhaber, Liddle, & Theobald, 2013; Ronfeldt & Campbell, 2016).

In our study, we provide a new approach to systematically understanding TEPs in Michigan first by observing variation in the types of TEPs that educate Michigan teachers, then


“…We provide a new approach to

systematically understanding TEPs (Teacher

Education Programs) in Michigan…”


by linking this variation to teacher distribution patterns, attrition, and mobility. We observe institutional and programmatic characteristics that are associated with teachers’ likelihoods of working in high-need schools, leaving the profession, and transferring. Although variation in TEP quality and outcomes has been identified through previous work, our study makes a contribution to the field for several reasons. Our approach to clustering similar TEPs together and looking at their outcomes specifically with respect to employment placement and retention together is unique. We carefully consider teachers’ individual characteristics as well as their post-secondary experiences in predicting employment outcomes. And finally, to our knowledge, this study is the first to tie employment outcomes to TEPs in the context of Michigan specifically. This matters because Michigan

has seen a considerable decline among its public school teaching population in recent years, remains a highly segregated educational environment, and experienced significant economic instability through the recent Great Recession. In addition, the state is one of 14 states that in recent years began to link TEPs to the in-service outcomes of their graduates (Doherty & Jacobs, 2015).

Specifically, we examine the relationship between teacher education programs (TEPs), the types of schools that teachers work in, and transfer and attrition rates. We answer the following research questions:

1) How can teacher education programs in Michigan be systematically understood through commonly shared institutional characteristics? 2) What is the relationship between the type of teacher education program a teacher attends and the school in which he or she later works? 3) To what extent does the type of teacher education program a teacher attends predict later mobility and attrition rates? We find meaningful variation across TEPs in teachers’ employment outcomes. After

controlling for teachers’ individual characteristics, school context, particular types of TEPs and specific institutional characteristics continue to be associated with distribution, attrition, and transfer patterns. Our results have important implications for how TEPs prepare teachers in Michigan. The remainder of this paper is organized as follows: First, we review key literature and describe teacher preparation in Michigan. Second, we describe our sample, methods, and findings. Finally, we discuss implications for our results and conclude with suggestions for future areas of research.


“We examine the relationship

between teacher education

programs (TEPs), the types of schools that

teachers work in, and transfer and

attrition rates.”


II. Background The following review of literature offers evidence of inequitable teacher distribution and teacher mobility patterns that disproportionately impact marginalized student populations. We also review existing research on the relationship between post-secondary teacher preparation and later employment outcomes and discuss teacher education programs in Michigan, the site of our study.

II.A. Teacher DistributionThere is a substantial body of evidence demonstrating that students do not have equal

access to teachers and that teachers are inequitably distributed in a way that disadvantages at-risk students in high-need schools. Data from the federal government indicates that students have unequal access to teachers across a number of measures. Schools with the highest shares of students of color have 50% more first-year teachers than those with the highest portions of White students and English Language Learners are also more likely to attend schools with high numbers of novice teachers (U.S. Department of Education Office for Civil Rights, 2014). Federal reporting also indicates that 6.7% of Black students are enrolled in schools where at least 20% of teachers do not hold proper certification, as compared to only 1.5% of White students. This phenomenon has been documented for a number of years. In 2002, Lankford, Loeb, and Wyckoff found that students in New York had disparate access to teachers, with low-achieving, low-income, non-White students from urban areas being taught by the least skilled teachers. Clotfelter, Ladd, and Vigdor (2005) found similar patterns in North Carolina, with students of color also being taught by less qualified teachers. Recently, Goldhaber, Lavery, and Theobald (2015) found inequitable teacher distribution to be a pervasive problem in Washington as well.

II.B. Teacher Mobility In addition to considering how teachers are distributed both across and within schools, teachers’ mobility and attrition rates also play a role in students’ access to educators. In addition to disparities in teacher quality, students are also differentially impacted by teacher turnover. A number of studies have found that less effective teachers are most likely to leave their schools (Cowen & Winters, 2013; Feng & Sass, 2016; Goldhaber, Grossman, & Player, 2010; Hanushek, Rivkin, & Schman, 2016) and that this has a disproportionate impact on lower-achieving schools (Feng, 2009; Hanushek et al., 2016). New evidence also suggests that the highest performing



teachers are more likely to leave as well. Feng and Sass (2016) observed that teachers in the top and bottom quartiles leave at higher rates than those in the middle. While turnover has a negative impact on schools, Haunshek and colleagues (2016) argue that this impact can be fully explained by the loss of productivity and experience levels when new staff are brought in as opposed to a drop in objective teacher quality. Similarly, Ronfeldt, Loeb, and Wyckoff (2013) argue that the turnover process itself is disruptive, irrespective of the effectiveness of the teachers who leave. Given this evidence, it follows that a reduction in rates of teacher transfer and attrition is a potentially desirable strategy for improving student outcomes.

II.C. Post-Secondary Educational Experiences In recent years, there has been an increasing interest in holding teacher education programs (TEPs) accountable for the outcomes of their graduates. We frame our study in this current era of increased accountability and it is against this backdrop that the questions we ask in this study stand out.

One example of the recent push towards teacher preparation accountability is work done by the National Center for Teacher Quality. The NCTQ has proposed a number of standards on which to assess preparation programs. NCTQ assesses TEPS on inputs, such as clinical experiences and course content, as well as outputs, such as how graduates impact student learning. A recent report on elementary education preparation recognized some national improvement but argued that teacher preparation still has a long way to go in meeting the standards that NCTQ has proposed (Lubell & Putnam, 2016). NCTQ’s approach to assessing TEPs has been scrutinized by a number of scholars criticizing a thin base of evidence to support it, reliance on website artifacts, and emphasis on inputs rather than outcomes (Cochran-Smith et al., 2016; Darling-Hammond, 2013; Fuller, 2014). For the purposes of this study, we remain neutral on the NCTQ approach itself, but raise NCTQ here simply as evidence of increasing attention being paid to teacher education programs.

We also note the increased emphasis placed on TEP performance by the U.S. Department of Education in recent years. Regulations released in October 2016 called for states to: track placement and retention of their recent gradates, both overall and in high-need schools; collect feedback from graduates and employers about program effectiveness; and track student learning outcomes (U.S. Department of Education, 2016). While these regulations were later overturned, they serve as additional evidence of the increasing focus on teacher preparation.

The focus on TEPs as one determinant of teacher outcomes has some basis in the empirical literature. A growing body of literature has demonstrated substantial teacher preparation effects. Research from Washington State has demonstrated that there are large



differences in attrition and transfer rates between TEPs (Goldhaber & Cowan, 2014) and that these programs explain differences in student achievement on state reading tests (Goldhaber, Liddle, & Theobald, 2013). Differences in TEPs have also been detected through classroom observation scores and subsequent student test scores of beginning teachers in Tennessee (Ronfeldt & Campbell, 2016).

Although teacher preparation has been linked to later effectiveness (Darling-Hammond et al., 2005) there is some debate as to the mechanism by which teacher preparation influences student outcomes. Student teaching placements may play a role. Ronfeldt (2012) found that teachers in New York City who completed student teaching in easier-to-staff schools were more successful later in their careers, both in terms of attrition and student achievement, even when they later worked in hard-to-staff schools. Other elements of teacher preparation that have been tied to effectiveness are selective admissions policies, rigorous content requirements, and structured feedback on classroom management (Lubell & Putnam, 2016). Finally, a teacher’s own perceptions of how well her TEP prepared her for the profession also predicts her likelihood of persisting (Rots et al., 2007).

Yet, not all studies of teacher preparation find that training explains later outcomes. A 2015 study in Missouri by Koedel, et al. found that teacher effectiveness is explained by individual teachers rather than the programs they attended. The authors of this study suggest that the lack of variability in teacher preparation programs may be due to the fact that a formal evaluation method has yet to be developed. Similarly, Lincove, et al. (2015) examined student outcomes by TEP in Texas and found no overall differences but did find differences by subject area and student types, suggesting the need to better identify specific strengths and weaknesses of programs. Finally, Lincove, et al., (2014) caution against the use of value-added modeling to link student outcomes to TEPs.

II.D. Michigan Context In our study, we focus specifically on teacher preparation in Michigan. According to the U.S. Department of Education’s (2015) Title II Report, Michigan had 39 teacher preparation providers in 2013-2014: 36 offering traditional preparation, three offering alternative certification, 36 offering undergraduate programs, and 26 offering postgraduate programs.


“The authors of this study suggest

that the lack of variability in

teacher preparation

programs may be due to the fact

that a formal evaluation method

has yet to be developed.”


Although there are a number of ways in which Michigan’s context is unique, recent events in the state mirror larger national trends. A package of reform laws passed in July 2011, which led to the adoption of a statewide teacher evaluation system and a significant reduction in tenure and collective bargaining protections (State of Michigan, 2011). Many other states have also adopted teacher evaluation laws in recent years. The number of states requiring teachers to be evaluated increased from 15 to 43 from 2009 to 2015 (Doherty & Jacobs, 2015). Furthermore, although no state linked evaluation to tenure decisions in 2009, 23 states did so in 2015.

Education reform in Michigan came on the heels of a significant recession in Michigan, in which the state experienced major economic decline and population loss. While this period of recession affected the entire nation, Michigan was particularly hard hit (Yagan, 2017). During this same period of time, Michigan began experiencing a significant drop in teacher certification rates. Indeed, there has been a 62% decrease in the number of initial teaching certificates issued annually in Michigan from 2003-2004 to 2016-2017 (Stackhouse, 2017). Furthermore, public sector employment in Michigan is low compared to other Midwest states and the nation as a whole. For state and local public sector employment combined, Michigan

has the lowest employment rate in the country, at 184 per 10,000 (U.S. Census Bureau, 2014). Michigan also fell far below the national average on the most recent National Assessment of Educational Progress (NAEP), a nationally representative exam. In 2015, Michigan ranked 41st in fourth-grade reading, 42nd in fourth-grade math, 31st in eighth-grade reading, and 38th in eighth-grade math (U.S. Department of Education. Institute of Education Sciences, National Center for Education Statistics, 2015). Performance on the NEAP has declined considerably in Michigan in recent years across all of these measures, particularly in fourth-grade. Michigan fell 15 spots from 27th to 42nd in fourth-grade math and 13 spots from 28th to 41st in fourth-grade reading from 2003 to 2015. Recognizing these challenges, the state has placed an increasing emphasis on improving educator quality in recent years. In 2009, the state began rating TEP performance across a number of measures, issuing Educator Preparation Institution score reports for every teacher preparation program in the state (e.g., Michigan Department of Education, 2016a). Programs are evaluated based on candidates’ licensure exam performance, surveys of candidates and supervisors, and performance of early-career teachers. This practice is reflective of larger national trends. As of 2015, 14 states, including Massachusetts, Texas, and Florida, linked in-service teacher performance to preparation programs (Doherty & Jacobs, 2015).


Michigan has seen a 62% drop in the number of annual

teaching certificates issued

from 2003–04 to 2016–17.


Additionally, the State Board of Education adopted an initiative to improve Michigan’s educational performance. Announced in February 2016, Michigan’s Top 10 in 10 initiative is intended to make the state a top-performing state in the coming years by achieving a number of goals. Of particular relevance to this study is Goal 3: Develop, support, and sustain a high-quality, prepared, and collaborative education workforce (Michigan Department of Education, 2016b). Included in this goal are strategies focused on incentivizing teachers to remain in the profession and strengthening teacher training.

Although there are a number of regulations set forth by the state that all TEPs must abide by, programs also have the flexibility to customize their programs in several ways. For instance, while all programs must require at least a 2.5 GPA for admission, a number of programs have more rigorous requirements, with several programs in the state requiring at least a 3.0 for incoming students. Similarly, some programs require interviews, letters of recommendation, and personal statements at entry and/or exit and others do not. In the case of student teaching requirements, there is substantial variation in the number of hours that programs require. While some programs require a minimum of 420 hours, others go well beyond that, up to 950 hours in the case of one institution. There is similar variation in the case of hours spent in other clinical experiences prior to student teaching. Programs also vary substantially in the credentials they offer. For example, some institutions offer as many as 50 credentials in areas ranging from Cognitive Impairment to Construction Trades to Family and Consumer Sciences; while others only offer a handful of programs in core academic areas. TEPs in Michigan also differ in size and geographical location. Some institutions had fewer than 20 enrolled students in 2013-14 and others had over 1,000. Although no programs are located in Northern Michigan (defined roughly as the northern third of Michigan’s lower peninsula), there are several in the Upper Peninsula and the remainder are dispersed across the lower portion of the Lower Peninsula. Given this variation in TEPs, prospective teachers are likely to have varying post-secondary experiences prior to entering the teaching profession. As a result, we are able to study how variation in preparation relates to later teacher placement and mobility outcomes.

II.E. The Current StudyThus, although past research has demonstrated some relationship between post-

secondary institutions and later career outcomes for teachers, this study is unique in that we


“…we identify specific

characteristics of programs in

Michigan that explain where

teachers work, their transfer

behaviors, and how long they stay in the profession.”


identify specific characteristics of programs in Michigan that explain where teachers work, their transfer behaviors, and how long they stay in the profession. Controlling for teachers’ personal characteristics, we identify specific institutional attributes that predict whether teachers work in high-need schools and how often they transfer or exit the profession. Our results suggest a number of implications for TEPs. If we know which types of institutions are more likely to produce graduates that go into high-need schools, steps can be taken to ensure they have the preparation they need to be successful. Additionally, if particular types of TEPs are producing graduates that persist for longer in their positions or the profession as a whole, then their approaches to preparing teachers might be applied in other post-secondary settings that have previously had less successful outcomes.

III. Data Our data contain the population of early-career K-12 teachers in their first five years of

teaching in Michigan public schools from 2011-12 through 2015-16, resulting in an analytic sample of 23,052 teachers in the administrative records provided by the Michigan Department of Education (MDE) and the Center for Educational Performance and Information (CEPI). To protect institutional and potentially, individual privacy in cases of small samples, we limited the data to teachers who attended one of the 19 largest TEPs in the state. All together, these TEPs accounted for approximately 85% of teacher-year observations in the state, with the remaining 15% spread in small numbers across smaller programs. Each of the 19 programs had at least 200 unique teachers, ensuring a sufficient sample size that would allow for meaningful analysis. Due to their unique exit and transfer behaviors, we conduct all of our analyses separately for traditional public school (TPS) and charter school teachers.

For each of the 19 TEPs in our analysis, we gathered data on institutional characteristics from the National Center for Statistics’ Integrated Postsecondary Education Data System (IPEDS) and TEP-specific characteristics from federal Title II reports. All IPEDS and Title II data is publicly available and from 2013-14, unless otherwise noted in table footnotes. We also use Educator Preparation Institution (EPI) scores from 2014 to 2016. Generated by MDE, these scores evaluate TEPs based on their students’ state licensure exam scores, survey responses from teacher candidates and supervising faculty, and effectiveness ratings of their graduates.


“If we know which types of

institutions are more likely to

produce graduates that go

into high-need schools, steps can be taken to ensure

they have the preparation they

need …”


Finally, we collected institution mission statements from TEPs’ websites and coded these for major themes. Keywords from each mission statement describing the goals of the institution and experiences of students were identified and then grouped together into nine major themes: Global/International, Reference to Faith, Inclusion, Community, Citizenship, Academic Focus, Career Focus, Service/Giving Back, and Diversity. A mission statement was coded as having a particular theme if it included the exact words listed above, a variation of these words (e.g. “diverse” instead of “diversity”), or other words or phrases with a similar meaning. For example, a mission statement stating that an institution prepares students to serve the world was coded as having a Global/International theme. We include mission statements in our analyses because these themes capture institutional purpose, giving some indication of the unique experiences that students have in each setting. Prior research has found that mission statements vary by institutional control and Carnegie classification (Morphew & Hartley, 2006) and therefore, we sought to account for institutional variation through mission statement themes. These themes as well as EPI scores and other institutional characteristics are summarized in Table 1 and discussed in further detail in the following section.

IV. Methods and Results

IV.A. Characteristics of Teacher Education Programs in Michigan To answer our first research question, How can teacher education programs in Michigan be systematically understood through commonly shared institutional characteristics?, we completed a Ward’s linkage cluster analysis so as to generate homogenous groupings of TEPs based on a number of institutional characteristics. Through this hierarchical approach to cluster analysis, TEPs are gradually grouped together in such a way that variance between clusters is maximized and the correlations among various TEP characteristics in the same cluster are maximized (Hamilton, 2009). TEPs grouped into one of four clusters and the summary statistics for each cluster are provided in Table 1. These clusters vary in a number of ways. Upon review of the mean characteristics of each cluster, we generated the following four labels to describe TEPs in Michigan: Small Private Religious Institutions with a Focus on Service; Midsize Regional Programs with a Focus on Local Community; Public Research Universities with Large Teaching Programs; and Academic Powerhouses with Affluent, High-Achieving Students. These clusters are described in more detail below:

• Small Private Religious Institutions with a Focus on Service (six programs) are the

smallest programs both in terms of teacher education and overall enrollment. Relative to other clusters, this group of institutions has the least diverse TEP students, with fewer



males and African American students than any other cluster. All institutions are private and all but one are religiously oriented. These programs have a particularly high emphasis on service and giving back to the local community relative to other TEPs. These programs require the fewest pre-student teaching and student teaching hours and do not specify any requirements for hours of mentorship.

• Midsize Regional Programs with a Focus on Local Community (five programs) are

public and have the second smallest student enrollment numbers, both in terms of teacher education and overall institution size. These programs require more hours of pre-student teaching and student teaching that the first cluster and also require more mentorship than any other TEP cluster. These programs are regional and place an emphasis on giving back to the local community in their mission statements. These programs have the lowest ACT scores and graduation rates as well as relatively high admissions rates. Institutions in this cluster have the highest portion of students receiving federal Pell grants, a proxy for financial need.

• Public Research Universities with Large Teaching Programs (six programs) are

significantly larger than institutions in the previous two clusters. These institutions have the largest TEP enrollment numbers, with more African American students than other TEP clusters. These programs require a relatively high number of hours of student teaching and require more pre-student teaching hours than other clusters. Median GPAs at graduation from these programs are the lowest of any cluster.

• Academic Powerhouses with Affluent, High-Achieving Students (two programs) are the

top-performing institutions academically in our study. ACT scores, graduation rates, and EPI scores are significantly higher than other clusters. These institutions have the largest overall enrollment numbers and second largest TEP enrollment numbers. Students have less financial need than those in other clusters and these institutions also have the most students of color overall. TEPs in this cluster require the most student teaching hours and the second highest number of pre-student teaching hours. TEP students in this cluster have the highest GPAs both at entrance and graduation.



IV.B.



Teacher Education Programs and Teacher Placement Observing significant differences between TEP clusters, we used these groupings to examine the distribution of teachers across school type to answer our second research question, What is the relationship between the type of teacher education program a teacher attends and the school in which he or she later works? Because we were specifically interested in the proportion of teachers from particular TEP clusters working in schools with at-risk student populations, we calculated the proportion of teachers working in four types of schools: Title I, priority, urban, and rural. Title I is a federal designation based on the portion of students qualifying for free or reduced-price a lunch, a proxy for poverty. Priority schools are those performing in the bottom 5% of the state based on standardized test outcomes. We considered all schools ever receiving a “priority” label in our analyses of this school type. Although urban and rural schools are mutually exclusive categories, we acknowledge that there is substantial overlap between priority schools, Title I schools, and each of these measures of locale. Each of these school types, however, indicates a unique set of needs and challenges: Title I is a designation based strictly on socioeconomic status, priority schools have experienced chronic low academic performance, and urban and rural locales each experience their own unique challenges to staffing identified in the literature (e.g., Cowen et al., 2012; Lankford, Loeb & Wyckoff, 2002). These results can be found in Tables 2a and 2b, where 2a describes TPS teachers and 2b describes charter school teachers. These tables include the overall share of teachers trained by each TEP cluster as well as the portion of observations they account for in Title I, priority, urban, and rural schools.



As illustrated in Table 2a, we found substantial variation in the portion of TPS teachers

working with at-risk students by TEP cluster. For example, while 6.43% of early-career TPS teachers in Michigan are in schools that have ever received a “priority” designation, TPS teachers trained in Midsize Regional Programs with a Focus on Local Community work in schools with “priority” designations at lower rates. Only 4.25% of TPS teachers from this type of TEP are found in priority schools. TPS teachers attending Public Research Universities with Large Teaching Programs worked in Title I, priority, and urban schools at higher than average rates. TPS teachers trained in Academic Powerhouses with Affluent, High-Achieving Students are overrepresented in urban schools, yet they work in Title I and rural schools less frequently. Statewide, 63.47% of early-career TPS teachers are in Title I schools, but in this particular cluster the rate is only 54.65%. Similarly, only 16.92% of TPS teachers in this group work in rural schools, as compared to 23.53% statewide.

We also found variation in teaching placements among charter school teachers. For example, placement in schools ever receiving a “priority” designation range from 7.80% in Small Private Religious Institutions with a Focus on Service to 12.45% in the case of Academic Powerhouses with Affluent, High-Achieving Students. Similarly, rural placement rates for charter school teachers range from 7.78% for Academic Powerhouses with Affluent, High-Achieving Students to 13.13% for Midsize Regional Programs with a Focus on Local Community and urban placement rates range from 34.70% for Small Private Religious



Institutions with a Focus on Service to 50.80% for Academic Powerhouses with Affluent, High-Achieving Students.

We conducted chi-square tests of independence to determine whether teacher placements were significantly different across TEP types. Across all four school types and both sectors, placement rates were found to be significantly different (p<.0001). We note that these are descriptive differences only, and we do not infer a causal link between type of institution attended and placement, per se. Still, these data provide an important summary indicator of which types of institutes place students in which Michigan public schools.

IV.C. Teacher Education Programs and Teacher MobilityWith this summary consideration of the distribution of teachers across TEP cluster and

school type, we turn to exit and transfer rates of teachers to answer our third research question, To what extent does the type of teacher education program a teacher attends predict later mobility and attrition rates? Answering this question is more complicated than the analyses above because, as Table 1 indicates, there are substantial differences in teacher demographics and other characteristics of the pre-teachers served by each TEP type. At minimum we need to account for key teacher demographics, which have been shown in the literature above to predict teacher placement and later mobility behavior. Thus to adjust the relationship between a teacher’s TEP characteristics and later mobility by that teacher’s demographic characteristics, we estimate logit models that predict exit and transfer rates for teachers based on gender, race, years of experience, position type, year in the data, school context, and region of the state. Position type includes whether or not a teacher’s primary teaching assignment is in math or science and whether or not the teacher is licensed to teach secondary grade levels. School context includes student racial and socioeconomic demographics and locale. We account for region of the state with intermediate school district (ISD) fixed effects. We estimated exit and transfer rates separately by sector. Descriptive statistics of teachers included in the analytic sample can be found in Appendix A. Our model is as follows:

(Eq. 1) P(ExitTransferit) = β0 + β1MALEi + β2RACEi + β3EXPERIENCEit + β4MATHSCIt +

β5SECONDARYt + β6YEARt + β7SCHOOLit + β8ISDit + εit

ExitTransfer is a series of exit and transfer outcomes for each teacher i, including: exiting the profession, defined as no longer appearing in the dataset; intra-district transfer, defined as being observed in a different school within the same district; and inter-district transfer, define as being observed in a different school district. Measures of exit and transfer are from one school year to the next, and we allow for non-consecutive years in the case of teachers who enter, leave, and re-enter the data between 2011 and 2016. Within each of these three types of



mobility, we also consider exit and transfer rates by school type, such as transferring from a Title I school to a non-Title I school or transferring from a priority school to another priority school. MALE is a binary indicator of teacher gender and RACE indicates teacher race. EXPERIENCE is the number of years a teacher is observed in the data. MATHSCI AND SECONDARy indicate whether a teacher’s primary assignment is in math or science and whether he or she teaches in secondary grade levels, respectively. YEAR is a series of binary indicators for year in data, 2011-12 through 2015-16. SCHOOL is a vector of school characteristics, including student socioeconomic status, race, English Language Learner rate, and special education rate. ISD is a vector of fixed effects for the intermediate school district employing each teacher. We estimate Equation 1 to fit predicted exit/transfer rates, which are in essence rates adjusted for teacher and school demographics. We summarize these adjusted exit/transfer rates by TEP cluster in Tables 3a and 3b. Table 3a contains the rates for TPS teachers and Table 3b summarizes charter school teacher exit and transfer rates.



A number of patterns can be observed in Tables 3a and 3b. With the exception of two measures of intra-district transfer for charter school teachers where sample sizes were small, all other exit and transfer rates were found to be significantly different across clusters (p<.0001). We also conducted a series of two-sample t-tests where we compared each cluster’s exit or transfer rate to the remaining three. As indicated in Tables 3a and 3b, bolded values with an asterisk represent significantly different values.

Focusing first on our results for TPS teachers in Table 3a, we found that all three measures of exit are highest from Midsize Regional Programs with a Focus on Local Community. In contrast, teachers trained in Small Private Religious Institutions with a Focus on Service have lower exit rates across these same three measures. We also found that overall intra-district and inter-district transfer rate are highest in Midsize Regional Programs with a Focus on Local Community. Transfer rates are generally lower in Small Private Religious Institutions with a Focus on Service and Academic Powerhouses with Affluent, High-Achieving Students.

Our results for charter school teachers, shown in Table 3b, demonstrate different exit and transfer patterns than TPS teachers. Across all measures, charter school teachers exit and transfer more frequently than TPS teachers. However, in addition to these general differences, there are also different trends by TEP cluster. For example, teachers trained by Academic Powerhouses with Affluent, High-Achieving Students have higher exit and transfer rates than average across many measures. This is in contrast to below average exit and transfer rates found among TPS teachers in Table 3. Similarly, while TPS teachers trained in Midsize Regional Programs with a Focus on Local Community had higher than average exit and transfer rates, charter school teachers from these programs have lower exit rates and inter-district transfer rates than average.

Overall, these results demonstrate significant variation in mobility and attrition rates by TEP type, even after accounting for teacher characteristics, school context, and regional differences.

Institutional Characteristics and Employment Outcomes After this descriptive examination of the variation in exit and transfer rates by TEP cluster, we next sought to identify specific institutional characteristics associated with these outcomes. We collapsed our demographically-adjusted predictions of teacher exit by TEP and estimated a series of bivariate regression models in order to determine the extent to which basic



locational, demographic and economic characteristics of institutions were related to exit and transfer. These models took the following format:

(Eq.2) = β0 + β1DEMOGRAPHICSj + εj

is the mean predicted exit or transfer rate of teachers in TEP j pooled over all years; β0 is the

constant term; DEMOGRAPHICSj is a series of measures of the TEP: locale, sector, institution size, racial composition, and financial need of students; and εj is the error term. We ran separate

models for TPS and charter school teachers. The results of these analyses are in Table 4. Each coefficient is an estimate of the bivariate relationship between a given outcome and institutional characteristic. For example, the bottom left coefficient in the table, 0.046, indicates that TEPs with higher proportions of Pell Grant recipients are associated with increased exit rates from TPS of 0.046 standard deviations. We identified a number of other statistically significant relationships between institutional demographics and employment outcomes as well. Most notably, charter school teachers who attended private institutions are less likely to exit the profession or transfer out of the district. Charter school teachers who attended larger institutions, on the other hand, are more likely to exit and transfer out of the district.

Institutional Rigor, Mission Statements, and Employment OutcomesNext, we estimated the extent to which measures of institutional rigor and mission

statement themes were associated with exit and transfer. We estimated multivariate relationships between each measure of rigor and mission statement theme with exit and transfer rates, controlling for the institutional covariates used in Table 4. These models took the following form:

(Eq.3) = β0 + β1RIGOR/MISSIONj + β2DEMOGRAPHICSj +εj



is the mean predicted exit or transfer rate of teachers by TEP; β0 is the constant term; RIGOR/

MISSIONj is either a measure of rigor: EPI score, ACT score, admissions rate, or graduation rate; or binary variable indicating the presence or absence of one of the following mission statement themes: Global/International, Reference to Faith, Inclusion, Community, Citizenship, Academic Focus, Career Focus, Service/Giving Back, or Diversity; DEMOGRAPHICSj is the vector of demographic measures of the TEP found in Equation 2: locale, sector, institution size,

racial composition, and financial need of students; and εj is the error term. Results from these

analyses can be found in Table 5.



We found that there were a number of significant associations between TEP characteristics and early-career teachers’ predicted exit and transfer rates, after controlling for other institutional demographics. First, higher EPI scores, a measure of program quality, are associated with lower exit and transfer rates for both TPS and charter school teachers across a number of coefficients. For example, higher licensure exam scores (“EPI Scores – MTTC” in Table 5) are associated with both a decrease in attrition and inter-district transfer among TPS teachers. Higher overall EPI scores are also associated with lower exit rates in TPS and lower inter-district transfer in both sectors. In contrast, higher ACT scores and higher graduation rates are associated with higher attrition rates for charter school teachers.

A number of mission statement themes were associated with significant differences in mobility and attrition rates as well. Institutions with an emphasis on service and diversity were observed to have higher exit and inter-district transfer rates for TPS teachers. Themes of inclusion and citizenship, on the other hand, were associated with lower transfer and exit rates. Finally, programs whose mission statements referenced faith were associated with lower exit and inter-district transfer rates, but also higher intra-district transfer rates specifically in the case of TPS teachers. Recall that all of these rates have been adjusted as described above by teacher and school demographics prior to being modeled here, suggesting that these TEP-related differences in teacher mobility are not driven by teacher race, gender, position type, experience level, or school context. TEP-Specific Characteristics and Employment Outcomes Finally, we considered the extent to which TEP-specific characteristics explained exit and transfer rates. Similar to our analyses in Table 5, we estimated two sets of models, one for TPS teachers and one for charter school teachers. These analyses were estimated using the following model:

(Eq. 4) = β0 + β1TEPCHARj + β2DEMOGRAPHICSj + εj

is the mean predicted exit or transfer rate of teachers in TEP i; β0 is the constant term;

TEPCHARj is a measure of TEP rigor, student demographics, or other program requirements; DEMOGRAPHICSj is the same vector of demographic measures of the TEP appearing in Equation 3: locale, sector, institution size, racial composition, and financial need of students; and εj is the error term. Results from these analyses can be found in Table 6.



Afteraccountingforinstitutionaldemographics,weidenti6iedseveralTEP-speci6iccharacteristicsassociatewithexitandtransferbehaviors.HigherminimumGPArequirementsandmedianGPAatentranceintoteachereducationareassociatedwithlowerattritionratesinthecharterschoolsector.AnumberofmeasuresofdiversityinTEPprogramdiversityareassociatedwithincreasedmobilityandattrition.Charterschoolteacherswhoattendedprogramswithmorewhitestudentsexitedtheprofessionlessfrequently,whileteacherswhoattendedprogramswithmoreHispanic,Asian,AfricanAmerican,andmalestudentsweremorelikelytoexitandtransferacrossanumberofmeasures.WealsonotethatprogramswithmorerigorousstudentteachingrequirementstraincharterschoolteacherswithlowerattritionratesandTPSteacherswithlowerintra-districttransferrates.Asabove,recallthatalloftheserateshavebeenadjustedasdescribedabovebyteacherdemographicspriortobeingmodeledhere,suggestingthattheseTEP-relateddifferencesinteachermobilityarenotdrivenbyteacherrace,gender,positiontype,experiencelevel,orschoolcontext.



V. Discussion

TEP Clusters and Employment OutcomesIn this paper, we found that particular aspects of TEPs in Michigan predict where teachers

work, how long they stay in the profession, and how frequently they transfer, both within and between districts. Through cluster analysis, we identified groups of TEPs that shared similar characteristics as well as employment outcomes. In the following subsections, we summarize and discuss the trends we observed in each TEP cluster.

• Small Private Religious Institutions with a Focus on

Service tend to train more TPS teachers who go on to teach in high-need schools, specifically Title I, priority, and rural schools. Charter school teachers trained by these programs, however, are less likely to go on to teach in high-need schools, with the exception of rural schools. Graduates of these programs exit the profession at lower rates than expected across all school types relative to statewide attrition rates. Across most measures of transfer, teachers trained in these programs also transfer less frequently than other early-career teachers in the state.

• Midsize Regional Programs with a Focus on Local

Community train higher than average numbers of teachers who go on to work in Title I and rural schools, but fewer who are employed by priority and urban schools. TPS teachers who attended TEPs in this cluster are more likely to quit the profession that their peers in other clusters and also transfer both within and across districts at higher than expected rates.

• Public Research Institutions with Large Teaching Programs train more teachers than

expected who go on to teach in Title I and urban schools in both sectors. Fewer teachers than average place into rural and priority schools. Teachers in this cluster exit the profession and transfer slightly more often than average.


“… Particular aspects of TEPs in

Michigan predict where teachers work, how long they stay in the profession, and how frequently

they transfer, both within and

between districts.”


• Academic Powerhouses with Affluent, High-Achieving Students train fewer teachers

who go on to teach in Title I and rural schools than expected across both sectors. Charter school teachers who attended this cluster of TEPs are more likely to quit the profession and transfer. In contrast, TPS teachers from this cluster are less likely to exit and transfer.

Each of these TEP clusters exhibits some potentially beneficial and some detrimental patterns with respect to the equitable distribution of teachers and to teacher retention. Midsize Regional Programs with a Focus on Local Community send higher numbers of their teachers to Title I and rural schools, while Academic Powerhouses with Affluent, High-Achieving Students are less likely to train teachers who work in these settings. Yet, Academic Powerhouses do place a higher than expected number of their graduates in urban and Title I schools. Public Research Institutions with Large Teaching Programs and Small Private Religious Institutions with a Focus on Service fall somewhere in the middle in their placement rates.

While graduates of Small Private Religious Institutions with a Focus on Service and Midsize Regional Programs with a Focus on Local Community are more likely to transfer between schools, they also tend to have lower attrition rates across a number of measures, especially in the charter sector. In contrast, Academic Powerhouses with Affluent, High-Achieving Students train charter school teachers who quit the teaching profession at higher rates and transfer more frequently.

Putting these findings together, we conclude that these four TEP program types are making varying contributions to teacher distribution patterns, both in terms of where their graduates go on to work and how long they persist. Small Private Religious Institutions with a Focus on Service train teachers who go on to teach in a range of high-need schools and generally have lower exit and transfer rates across both sectors and all school types. Although teachers trained at Midsize Regional Programs with a Focus on Local Community also go on to teach at high-need schools at fairly high rates, their exit and transfer rates are higher than average across a majority of measures. Teacher placement and mobility for those who are trained at Public Research Institutions with Large Teaching Programs and Academic Powerhouses with Affluent, High-Achieving Students are mixed. While teachers from these programs place in urban schools at high rates, their placement in rural schools is low. Furthermore, teachers from Academic Powerhouses with Affluent, High-Achieving Students are observed less frequently in Title I schools. Whether these patterns are due to institution-specific plans or, perhaps more likely, due to the characteristics and preferences of pre-teachers who enter those programs, we are unable to fully disentangle here. These patterns do, however, suggest that for all types of



TEPs, there are areas in which their contributions to the equitable distribution of teachers could be strengthened.

Although the higher intra- and inter-district transfer rates we observe in some TEP types are problematic, we are more concerned by the attrition rates we observe among TPS teachers trained in Midsize Regional Programs with a Focus on Local Community and charter school teachers in Academic Powerhouses with Affluent, High-Achieving Students. Public Research Institutions with Large Teaching Programs also have higher than average attrition rates across both sectors. Although turnover is disruptive, teachers’ experience levels matter as well. Up to a point, teachers become more effective the longer they teach (Clotfelter, Ladd, & Vigdor, 2006; Feng, 2010; Rice, 2013), but if teachers are quitting the profession prematurely, then students are being disproportionately exposed to less effective educators. While we are unable to causally identify the specific attributes of these programs that may be leading to higher attrition rates, we can speculate that some of the similarities across these three clusters should be considered. In particular, these clusters contain TEPs whose institutions have larger enrollment numbers overall as well as larger TEP sizes. It may be the case that students in these programs receive a less individualized post-secondary experience, leading to lower rates of persistence in the teaching profession. It may also be true that larger programs have more inherent heterogeneity in the experiences of individuals who attend them, leading to more average turnover from later teaching positions simply because not all graduates of a large program may be expected to fit equally well in terms of either placement or even career choice in the first place. It is also the case that these students may have other labor market opportunities outside of teaching, perhaps because of their academic pedigrees. In any case, the results suggest that looking beyond initial placement but also to retention is an important step in determining TEP contributions to an equitable distribution of teachers.

Institutional Characteristics and Employment Outcomes In addition to our findings across TEP clusters, we also observed a number of notable trends in our regression models that estimated the relationship between institutional characteristics and employment outcomes. We considered how institutional demographics, rigor, mission statements, and TEP-specific measures predict exit from the profession, intra-, and inter-district transfer. While teachers who attended TEPs in more isolated locales tend to have higher exit and transfer rates, attending an urban or suburban TEP is associated more often with lower exit and transfer rates. These findings, however, may simply be due to our inability to fully account for individual teacher characteristics through the predicted rates of exit and transfer we estimated.



Institutions with more white students have lower exit and inter-district transfer rates while larger institutions and those with higher proportions of students receiving Pell grants have higher exit rates. TEPs with more Hispanic students have higher transfer rates and those with more African American students have higher attrition and transfer rates. These demographic trends are concerning, given the lack of diversity that already exists in the teaching workforce (National Center for Education Statistics, 2013) and the fact that turnover rates are significantly higher among teachers of color (Achinstein, Ogawa, Sexton, & Freitas, 2010; Partee, 2014). Indeed, administrative data records show that Michigan’s number of African American teachers in core classroom positions has declined 51% from 2005 to 2016. Our findings suggest that, although TEPs are by no means to blame for these trends, there is work to be done in the area of teacher training to combat the underrepresentation of teachers of color in Michigan’s schools.

Of particular interest is the fact that institutions with higher ACT scores and graduation rates, as well as those with an emphasis on service and diversity, are graduating teachers who exit the profession more frequently. This finding that successful teachers are exiting the profession more frequently aligns with recent work by Feng and Sass (2016) who found that teachers in the top quartile leave at higher rates than their average-performing colleagues. It also aligns with our own results here with respect to the higher exit rates among charter school teachers from strong academic institutions. Yet despite this relationship between higher academic performance and higher attrition, we found that TEPs whose graduates have higher GPAs upon program entry and completion have lower rates of charter school exit from the profession. However, since grading systems are not uniform across institutions, it is unclear whether higher GPAs represent higher academic performance or simply institutional norms that tend to lead to inflated grades.

Implications Across TEP clusters as well as institutional characteristics, we found a number of relationships between TEPs and employment outcomes. TEPs from particular clusters are associated with over- and under-representation in high-need schools as well as disproportionate rates of exit and transfer. We find that these trends correspond with institution locale, student


“… institutions with higher ACT scores

and graduation rates, as well as

those with an emphasis on

service and diversity, are

graduating teachers who exit

the profession more frequently.”


demographics, EPI effectiveness scores, and GPA. These findings have a number of implications for teacher distribution, exit and mobility, and the overall diversity of the teaching workforce. First, we revisit the fact that Academic Powerhouses with Affluent, High-Achieving Students are sending fewer of their students to high-need schools than other clusters. Specifically, teachers who were trained by TEPs in this cluster are far less likely to work in rural schools and in the case of TPS teachers, significantly less likely to work in Title I schools. Charter school teachers from these TEPs also have particularly high exit and transfer rates. Institutions in the cluster are the highest performing in the state and as such; we might expect that these institutions be well positioned to effectively address students’ access to strong educators. Yet despite the fact that these institutions have the highest average EPI scores, highest ACT scores, and most selective admissions policies in our sample, they do not on average appear to be able to encourage graduates to work with those students who have the greatest need. Our results imply that TEPs in this cluster might develop strategies to encourage more of their graduates to work in high-need schools and to better support those who are employed in the charter sector. Next, we consider the relationship we observed between academic rigor and attrition in the charter sector. After controlling for teachers’ individual characteristics, school context, and institutional demographics, we found that TEPs with higher average ACT scores and graduation rates have higher attrition rates among charter school teachers. Although this result is not unique to Michigan and mirrors a larger trend identified by Feng and Sass (2016) and Cowen and Winters (2013), this is certainly something that TEPs and the broader teacher policy arena ought to consider more seriously. Some observers have expressed concern that we are losing our strongest educators, and while the verdict is still out on that particular score, declining interest in the teaching profession (ACT, 2015) coupled with decreasing rates of enrollment in TEPs (Sawchuck, 2015) raise serious questions as to whether our supply of teachers is sustainable. These findings suggest that TEPs must carefully consider who they are recruiting into teaching and ensure that they have strategies in place to encourage their strongest graduates to persist in the profession.

Finally, we consider what our findings mean for diversity in Michigan’s teaching workforce. Programs with more students of color have weaker outcomes in terms of attrition and transfer. Previous research has indicated that all students benefit from teachers of color (Cherng & Halpin, 2016), suggesting the importance of developing new ways to ensure their persistence in the teaching profession.



Gains have been made in the recruitment of teachers of color in recent years, but these gains have been offset by disproportionately high rates of attrition (Partee, 2014). A recent study suggests that university-based induction may be one approach to reducing attrition in high-need schools, where teachers of color are more likely to work (Boyd, Lankford, Loeb, Ronfeldt, & Wyckoff, 2011). Bastian and Marks (2017) found that university-based induction in North Carolina reduced teacher turnover in high-need schools and identified a number of strengths to universities supporting beginning teachers in these settings. Those findings suggest that if TEPs, especially those serving more students of color, provide continued support to their recent graduates as they begin their careers, attrition and transfer rates may improve.


“… if TEPs, especially those

serving more students of color,

provide continued support to their

recent graduates as they begin their

careers, attrition and transfer rates

may improve.”


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