PRINCIPAL EFFECTIVENESS AND

PRINCIPAL TURNOVER

Jason A. Grissom

(corresponding author)

Peabody College

Vanderbilt University

Nashville, TN 37203-5721

jason.grissom@vanderbilt.edu

Brendan Bartanen

Peabody College

Vanderbilt University

Nashville, TN 37203-5721

brendan.bartanen@vanderbilt

.edu

Abstract
Research demonstrates the importance of principal eﬀectiveness
for school performance and the potentially negative eﬀects of
principal turnover. However, we have limited understanding of
the factors that lead principals to leave their schools or about
the relative eﬀectiveness of those who stay and those who turn
over. We investigate the association between principal eﬀective-
ness and principal turnover using longitudinal data from Ten-
nessee, a state that has invested in multiple measures of principal
performance through its educator evaluation system. Using three
measures of principal performance, we show that less-eﬀective
principals are more likely to turn over, on average, though we ﬁnd
some evidence that the most eﬀective principals have elevated
turnover rates as well. Moreover, we demonstrate the importance
of diﬀerentiating pathways out of the principalship, which vary
substantially by eﬀectiveness. Low performers are more likely to
exit the education system and to be demoted to other school-level
positions, whereas high performers are more likely to exit and to
be promoted to central oﬃce positions. The link between perfor-
mance and turnover suggests that prioritizing hiring or placing
eﬀective principals in schools with large numbers of low-income
or low-achieving students can serve to lower principal turnover
rates in high-needs environments.

https://doi.org/10.1162/edfp_a_00256

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Principal Effectiveness and Turnover

I N T RO D U C T I O N

1 .
A growing body of policy research documents the importance of the school principal
for school success (e.g., Branch, Hanushek, and Rivkin 2012; Coelli and Green 2012;
Grissom, Kalogrides, and Loeb 2015). Although principals have little direct inﬂuence on
student learning, they inﬂuence school-level factors, such as climate and human capi-
tal decisions, that indirectly aﬀect achievement (Sebastian and Allensworth 2012; Kraft,
Marinell, and Yee 2016; Burkhauser 2017; Cannata et al. 2017). Evidence also suggests
that turnover in the principal’s oﬃce can disrupt these processes. Several recent studies
ﬁnd that principal turnover is associated with both higher rates of teacher turnover and
lower student achievement gains in subsequent years, and may have even more nega-
tive eﬀects in schools with larger numbers of low-income and low-achieving students
(Béteille, Kalogrides, and Loeb 2012; Miller 2013; Wills 2016).

The link between principal turnover and negative school outcomes suggests that un-
derstanding principal turnover is critical for education research and policy. However,
studies in this area are surprisingly scarce, particularly in comparison to the volumi-
nous literature on teacher turnover (Guarino, Santibañez, and Daley 2006; Grissom,
Viano, and Selin 2016). Researchers’ incomplete understanding of the leadership labor
market means that we have little guidance to oﬀer policy makers interested in address-
ing leadership instability in schools.

There are at least two contributions this study makes in pushing forward our under-
standing of the principal labor market. First, we address an omission in studies of the
factors that predict principal turnover—the role of a principal’s eﬀectiveness on the job.
Existing studies leave open the question of whether turnover is concentrated among low
or high performers. Although principal turnover likely negatively aﬀects school perfor-
mance in the short-term (e.g., Miller 2013), it does not follow that all principal turnover
is detrimental; high turnover rates among low performers may actually promote school
performance in subsequent years. In contrast, if turnover is highest among high per-
formers, we may even be underestimating the costs of principal turnover, suggesting
that policy makers should make addressing principal turnover even more of a policy
priority, and perhaps implement policies targeted at retaining high performers.

Second, we move beyond dichotomous turnover measures to investigate how dif-
ferent factors predict diﬀerent pathways out of a school leadership position, includ-
ing moves to another principal position in the district, moves to other districts, exits
from education altogether, and—as a particularly novel aspect of our approach—moves
“downward” to assistant principal or classroom positions. Modeling these pathways
is important because of the complexity of administrators’ career behavior, particularly
with respect to the agency of that behavior (Farley-Ripple, Solano, and McDuﬃe 2012).
In contrast to studies of the teacher labor market, where turnover usually is discussed
as teacher-driven because involuntary moves or exits are relatively rare, agency for prin-
cipals is less clear. Principals are “middle managers” in the district bureaucracy (Morris
et al. 1982), and have fewer job protections; thus, they are likely to be more at risk of
being moved by the district than are teachers. In the absence of data on whether a move
is principal- or district-initiated (data that are typically unavailable), providing insight
on this agency is diﬃcult. Using measures of principal eﬀectiveness to predict diﬀer-
ent job outcomes can provide some suggestive evidence on this point. For example,
a ﬁnding that less-eﬀective principals are more likely to move to assistant principal

356

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Jason A. Grissom and Brendan Bartanen

positions would be consistent with districts demoting low performers. In contrast, a
ﬁnding that high performance predicts moves to other districts may be evidence that
districts compete to attract more eﬀective principals.

To be speciﬁc, we seek to answer two main research questions. First, to what extent
are more-eﬀective principals more or less likely to turn over, and does the association
between eﬀectiveness and turnover vary with how eﬀectiveness is measured? Second,
how are measures of eﬀectiveness associated with diﬀerent types of principal turnover,
such as moves within and across districts, exits from the education system, and demo-
tions to non-principal positions?

Our examination of the association between principal turnover and principal eﬀec-
tiveness utilizes data from Tennessee, which is a useful context for this study not only
because the state maintains robust administrative data on a large number of principals
and schools, but also because we can access multiple measures of principal eﬀective-
ness. First, we utilize high-stakes ratings given by principals’ supervisors as part of
the state’s administrator evaluation system, which has been in place since the 2011–12
school year. These ratings, which make up half of principals’ overall evaluation scores,
are based on a rubric that deﬁnes eﬀective principal practice. Second, we use data from
a statewide survey of teachers in which respondents were asked to assess the quality of
leadership in the school. In contrast to the evaluation ratings, these survey responses
are low-stakes and never observed by the principal or district. Third, we incorporate
scores from school value-added models of student achievement. Although research
suggests that school value added is not a valid measure of a principal’s eﬀectiveness
(Grissom, Kalogrides, and Loeb 2015; Chiang, Lipscomb, and Gill 2016), we include
these scores because of the emphasis Tennessee places on them as a performance mea-
sure in the administrator evaluation system and as a component of the accountability
system more generally. The use of multiple measures provides a more robust examina-
tion of the eﬀectiveness–turnover relationship. Moreover, testing the predictive power
of two measures of eﬀectiveness used in the state evaluation system is useful given
the growth of rigorous principal evaluations systems in many states in recent years
(Superville 2014).

Our results show that principals rated more eﬀective by their supervisors and teach-
ers and who lead schools with higher test score growth are signiﬁcantly less likely to
leave their schools, on average, conditional on a large set of principal and school char-
acteristics and district ﬁxed eﬀects. The supervisor evaluation rating results are robust
to the inclusion of school ﬁxed eﬀects. Looking beyond a binary measure of leadership
turnover, we ﬁnd that higher rates of turnover among low performers are driven pri-
marily by exits from the educational system and demotions to other school positions.
This latter form of turnover, overlooked in prior studies, is substantial, constituting ap-
proximately one-ﬁfth of all leadership turnover in the state, and suggests that school
districts consider job performance in determining who serves in school leadership po-
sitions. Moreover, we ﬁnd some evidence that the turnover-performance relationship
may be nonlinear, with an uptick in turnover due to exits and promotions to central
oﬃce associated with the highest evaluation ratings.

We proceed ﬁrst by reviewing the existing literature on principal turnover and ed-
ucator labor markets more generally, which provides a framework for our analysis. We
then describe the data, measures, and methods. Next, we describe the turnover results,

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357

Principal Effectiveness and Turnover

ﬁrst for a binary measure of turnover then for a multinomial measure. The ﬁnal section
concludes with the implications of the study for policy and practice and suggestions for
future research.

2 . L I N K I N G P R I N C I PA L E F F E C T I V E N E S S TO P R I N C I PA L T U R N OV E R
Research suggests that principal turnover has negative impacts on both teachers and
students. For example, Miller (2013) ﬁnds that student achievement declines prior to a
principal departure and continues to fall for several years after the new principal en-
ters the school. Compared with periods of stability within the same school, the aver-
age student scores 0.021 standard deviation (SD) lower on standardized tests in the
ﬁrst year of a new principal, which constitutes a substantive decrease when aggregated
across an entire school. This drop in achievement is concurrent with higher teacher
turnover rates, suggesting a mechanism whereby principal turnover can indirectly re-
sult in lower student outcomes.1 Wills (2016), using data from South Africa, ﬁnds simi-
lar negative short-term impacts on student outcomes, with eﬀects concentrated among
schools whose principals leave the education system. Finally, Béteille, Kalogrides, and
Loeb (2012) ﬁnd that students in low-achievement schools score 0.04 to 0.06 SD lower
on math and reading assessments when they have a new-to-school principal. Addition-
ally, they ﬁnd the average teacher is 10 percent more likely to leave that year, with sug-
gestive evidence that the relationship is stronger for teachers with higher value added.
These negative eﬀects help drive concerns that principal turnover in the United
States is too high (School Leaders Network 2014). In a summary of estimates from dif-
ferent states and districts, Béteille, Kalogrides, and Loeb (2012) report annual principal
turnover rates of between 14 percent and 36 percent, with variation that depends on
both data source and method of calculation. Recent estimates from the National Center
for Educational Statistics place principal turnover rates at 23 percent nationally2 (Sny-
der, de Brey, and Dillow 2016), which is about 7 percentage points higher than rates
reported for teachers. These rates are even higher in schools with large numbers of
traditionally disadvantaged students. For example, schools with fewer than a quarter
of students qualifying for free or reduced-price lunch (FRPL) have an annual turnover
rate of 18 percent, compared with a turnover rate of 26 percent in schools where more
than three quarters of students qualify.

3 . C O N C E P T UA L I Z I N G T H E R E L AT I O N S H I P B E T W E E N E F F E C T I V E N E S S

A N D T U R N OV E R

A large literature examines educator turnover. The overwhelming majority of these
studies focus on teachers, though a handful of studies have examined school leaders
(e.g., Gates et al. 2006; Papa 2007; Loeb, Kalogrides, and Horng 2010; Ni, Sun, and
Rorrer 2014; Cullen et al. 2016; Sun and Ni 2016) and district leaders (e.g., Grissom
and Andersen 2012; Grissom and Mitani 2016). Although not always made explicit, the

1. Miller notes that the increase in teacher turnover is relatively small, with the typical school losing one extra

teacher from a staﬀ of forty teachers in the ﬁrst year of a new principal.

2. This estimate comes from the 2011–12 Schools and Staﬃng Survey (SASS). The principal turnover rate was
slightly lower in the 2007–08 SASS (21 percent), which was the ﬁrst iteration of the survey that included a
turnover estimate.

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Jason A. Grissom and Brendan Bartanen

underlying framework for many turnover studies is an economic model that conceives
of educators as participants in an educator labor market. We adopt this perspective as
well.

In a simple labor market, employees’ turnover outcomes result from a two-sided
decision-making process in which both the employee (the supply side) and the em-
ployer (the demand side) weigh the costs and beneﬁts of an employee’s continued em-
ployment in a position, relative to the costs and beneﬁts of alternatives. Principals weigh
the beneﬁts and costs of remaining as the principal in their current school against the
(perceived) beneﬁts and costs of available alternatives, which might include moving
to another school in the district, moving to a school in another district, or exiting the
public school workforce for retirement or other employment. These beneﬁts and costs
are both pecuniary and nonpecuniary—that is, related to both monetary compensation
and working conditions. When the comparison of the “bundle” of pecuniary and non-
pecuniary costs and beneﬁts is less favorable in the current position than in a viable
alternative, the principal turns over; thus, lower compensation and factors associated
with less desirable working conditions, such as less support from the school’s commu-
nity, are likely predictors of principal turnover. Research on principal turnover supports
this hypothesis. For example, both Papa (2007) and Baker, Punswick, and Belt (2010)
ﬁnd that higher salaries are associated with lower principal turnover rates, and Loeb,
Kalogrides, and Horng (2010) and Sun and Ni (2016) document the expressed and re-
vealed preferences of principals to work in schools serving more advantaged student
populations, which other studies show tend to be correlated with better working condi-
tions (Loeb, Darling-Hammond, and Luczak 2005; Grissom 2011; Simon and Johnson
2015).

Most teacher and leader turnover research focuses on the educator side of the deci-
sion process, considering turnover decisions to be primarily voluntary and thus driven
by the factors that inﬂuence the teacher or principal’s assessment of current and al-
ternative job possibilities. In an era of high-stakes accountability and educator evalua-
tion, however, consideration of decisions on the employer (or labor demand) side has
become increasingly important. Further, demand-side considerations likely are even
more important for school leaders than for teachers, given principals’ position as mid-
dle managers whose employment opportunities within the district are more directly
controlled by district leaders (Morris et al. 1982). Analogous to the decision-making
process of the principal to stay or leave, school district leaders weigh the costs and ben-
eﬁts of retaining a principal in his or her current position against alternatives, such as
moving the principal to another school and ﬁnding a replacement. Thus, factors that
make continued employment of a principal in his or her school less attractive to the
district will be associated with higher turnover.

A principal’s eﬀectiveness likely is an integral component of the beneﬁt–cost calcu-
lation on both the supply and demand side. All else equal, workers are more satisﬁed
in jobs they perform at a high level (Judge et al. 2001), suggesting that principals are
more likely to want to stay in their schools when they are more eﬀective. At the same
time, more eﬀective principals may have more attractive outside options, including
other principal positions or work outside of public schools. The relative attractiveness
of alternative positions increases the opportunity cost of staying in the current posi-
tion, which may increase the probability that the principal leaves. On the district side,

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Principal Effectiveness and Turnover

a principal’s job performance is a factor in the beneﬁt–cost equation as well. Districts
may provide beneﬁts or incentives to high performers to encourage them to stay, and
presumably are more likely to remove principals from school leadership positions who
they assess to be ineﬀective. Yet a district may also perceive beneﬁts to moving an ef-
fective principal—for example, if they believe sending a high performer across town to
lead a persistently struggling school would increase overall district performance.

In short, predictions about the association between principal eﬀectiveness and bi-
nary turnover are ambiguous. Here, it is useful to deepen the analysis by expanding
beyond simple binary measures, as there are numerous pathways out of a principal
position. Principals can stay in the same role (i.e., in a principal position) but move to
another school within the same district or in another district. As discussed, eﬀective-
ness could have either a positive or negative association with these moves. Importantly,
principals can also change roles, regardless of whether or not they change districts.
We emphasize two types of role changes. The ﬁrst is a move to a central oﬃce lead-
ership position, which we call promotions. The second is a move into another school
role, such as assistant principal or teacher, which we call demotions. For promotions,
districts may wish to elevate a high-performing principal to a central oﬃce position if
such promotions are rewards or if they think the district overall would beneﬁt from
having a high-performing leader in a more centralized role. Alternatively, districts may
be less likely to promote high-performing principals if their perceived value is greatest
at the school level—particularly germane if possible replacements for the principal are
less eﬀective. Promotion may also be a means to remove an ineﬀective principal from
a school position. In contrast, we expect that the prediction for demotions is unam-
biguous: less eﬀective principals will be more likely to be moved “down” to assistant
principal or non-leadership positions.

Examination of the predictors of these various pathways out of the current principal
position is an important contribution of this study. To our knowledge, no quantitative
principal turnover studies have diﬀerentiated promotions and demotions from within-
and across-district moves and exits, though scholars have pointed out the importance
of this diﬀerentiation (Farley-Ripple, Solano, and McDuﬃe 2012; Miller 2013).

Additionally, our use of multiple measures of principal eﬀectiveness improves
upon previous research. The extant research is far from conclusive about the behav-
iors and strategies that deﬁne eﬀective school leadership, with studies linking prin-
cipal eﬀectiveness variously to instructional leadership (Robinson, Lloyd, and Rowe
2008; Grissom, Loeb, and Master 2013), positive school or learning climates (Sebas-
tian and Allensworth 2012; Burkhauser 2017), strategic management of human capital
(Milanowski and Kimball 2010; Grissom and Bartanen, 2019), and broad organizational
management skills (Grissom and Loeb 2011), among others. Various approaches to
measuring principal eﬀectiveness in diﬀerent turnover studies reﬂect this lack of con-
sensus about how best to conceptualize it. For example, Sun and Ni (2016) use the
2007–08 Schools and Staﬃng Survey to create an indirect measure they call “principal
leadership quality” from teacher responses regarding leadership practices, but this set
of items is small and may capture only some facets of principal leadership. Other stud-
ies (e.g., Cullen et al. 2016) do not measure leadership behaviors or practices but instead
use school-level value-added scores, which states and districts may value but are likely
poor proxies for principal eﬀectiveness (Grissom, Kalogrides, and Loeb 2015; Chiang,

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Jason A. Grissom and Brendan Bartanen

Lipscomb, and Gill 2016). We use both types of measures (teacher survey-based mea-
sures and school value added) but augment them with principals’ practice ratings given
by principals’ supervisors using a rubric based on state standards for eﬀective leader-
ship. These evaluation ratings potentially provide a more direct measure of principal
eﬀectiveness linked to a broader set of leadership behaviors. In addition, because prac-
tice ratings and value added contribute to districts’ oﬃcial assessments of leadership
quality, we expect that they may provide diﬀerent signals about principal career moves
than teachers’ survey-based assessments.

4 . DATA A N D M E A S U R E S
We make use of longitudinal administrative data ﬁles including information on all pub-
lic education personnel in Tennessee from the 2011–12 to 2014–15 school years, pro-
vided by the Tennessee Department of Education (TDOE) via the Tennessee Education
Research Alliance at Vanderbilt University. These ﬁles contain information about em-
ployees’ personal and professional characteristics, including job positions, gender, race
and ethnicity, years of experience, and highest degree earned. We used these ﬁles to con-
struct additional experience measures, such as years employed in their current school.
We then merged these data with information on the characteristics of the schools and
districts in which principals currently work from annual student demographic and en-
rollment data from TDOE and the National Center for Education Statistics’ Common
Core of Data ﬁles.

School and District Characteristics
Prior research ﬁnds that school contextual factors are important predictors of principal
turnover. Tennessee’s data system includes many characteristics of the schools in which
the principals work. In addition to enrollment size and level (i.e., elementary, mid-
dle, high, or other), we use information on student composition, including the propor-
tion of the school’s students who are black, Hispanic/Latino, FRPL-eligible, diagnosed
with a disability, and classiﬁed as gifted. We also construct a standardized achievement
index, which measures a school’s average student score across standardized assess-
ments administered in that school as part of the Tennessee Comprehensive Assessment
Program.3 Descriptive statistics for each of these measures are provided in Appendix
table A.1.4

Additionally, in some models we include the following district characteristics: locale
type (urban, suburban, town, or rural), the proportion of students who are black or His-
panic/Latino, and the number of schools operated by the district—all of which come
from the Common Core of Data—as well as the proportion of school-aged children liv-
ing in poverty within the district boundaries, which we obtained from the U.S. Census

3. Students in grades 3–8 take yearly assessments in reading, language arts, mathematics, science, and social stud-
ies. High school students take end-of-course assessments in Algebra I, Algebra II, English I, English II, English
III, Biology, Chemistry, and U.S. History. We construct the achievement index by standardizing student-level
scores within grade and year (subject and year for end-of-course tests), then computing weighted school-level
mean scores.

4. Tables are available in a separate online appendix that can be accessed on Education Finance and Policy’s Web

site at www.mitpressjournals.org/doi/suppl/10.1162/edfp_a_00256.

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Principal Effectiveness and Turnover

Bureau’s Small Area Income and Poverty Estimates. The summary of these character-
istics in table A.1 illustrates the wide variation in district contexts in Tennessee. For
instance, there are approximately equal numbers of urban and rural districts (30 per-
cent and 32 percent, respectively), with the smallest districts operating only one school
and the largest district operating 290 schools.

Principal Characteristics
We incorporate several principal characteristics from the personnel ﬁles: total salary,
gender, race (whether the principal is black or white),5 and age. Appendix table A.1
shows that 55 percent of Tennessee principals are women, 19 percent are black, and the
average age is 49.6 years. The average yearly salary is approximately $81,000. We also
use data on three measures of principal qualiﬁcations—tenure in the current school,
years of principal experience, and highest degree earned (i.e., bachelor’s, master’s, edu-
cational specialist, or doctoral degree). Because Tennessee’s longitudinal administrative
ﬁles are only available starting in 2003–04, we top-code length of tenure and prior prin-
cipal experience at eight years. As shown in table A.1, only 17 percent of principals have
worked at their school for at least eight years, and 28 percent have eight or more total
years of prior principal experience. Approximately half of Tennessee’s principals have
been in their current school for fewer than three years, and more than half have fewer
than ﬁve years of principal experience. We combine principals whose highest degree is
a bachelor’s or master’s degree into a single category, which constitutes approximately
56 percent of principals. Of the principals with more advanced degrees, 30 percent have
an education specialist degree and 13 percent have a doctorate.6

Measuring Principal Turnover
Turnover variables are constructed using the longitudinal job history ﬁle, which pro-
vides a yearly snapshot of every educator in Tennessee’s K–12 public school system.
For each year, we observe an employee’s job information, including title and school
placement. From these ﬁles, we create binary and categorical turnover variables. The
binary variable takes a value of 1 if a principal in school j in year t is not the principal
in school j in year t + 1, and zero otherwise.7 The categorical indicator expands to ﬁve
turnover types (not including principals who stay in their school): principals who move
to another principal position in the same district (within-district move)8; principals who
move to another principal position in a diﬀerent district (across-district move); principals

5. More than 99 percent of principals in Tennessee in 2011–12 through 2014–15 were black or white. We dropped

a total of twenty-eight nonblack, nonwhite principal-by-year observations from the analysis.
In our regression models, we combine education specialist and doctorate into a single category. Results do not
change when these degrees are categorized separately.

7. Under this deﬁnition, principals may have multiple turnover events within the observation period. Of the 2,400
unique principals we observe in the data, 42 percent have one turnover event; only 2 percent have multiple
turnover events. In cases of a school closure (i.e., we observe a school in the data in year t but not in year t + 1),
we drop from the analysis the principal in that school in its ﬁnal year. We observe roughly ten school closures
per year.

8. Charter schools are included in our data ﬁles and are considered part of the district in which they are located.

Moves to charter schools, then, are typically coded as within-district moves, rather than exits.

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Table 1. Principal Turnover Rates by Year

Pooled

2012

2013

2014

2015

Principal turnover

17.0%

20.2%

16.0%

16.4%

15.2%

Within-district move

Across-district move

Exit

Demoted

Promoted

3.3

0.6

6.8

3.0

3.2

5.1

0.7

8.1

3.1

3.3

2.8

0.2

6.7

3.0

3.3

2.8

1.2

6.4

3.1

3.0

2.4

0.4

6.2

2.9

3.2

6,532

1,655

1,653

1,621

1,603

who leave the Tennessee education system (exit)9; principals promoted to central oﬃce
positions (promoted); and principals demoted to school-level positions (demoted).10

Table 1 shows the principal turnover rates for the 2011–12 through 2014–15 school
years. The average yearly turnover rate is 17 percent. Among principals who leave their
position, 40% exit the Tennessee education system altogether. Thirty-six percent of
turnover cases are principals who change positions in year t + 1, with a roughly even
split between promoted and demoted principals. Principals who change schools (23
percent of turnover cases) are far more likely to move within a district than across
districts.11

Measuring Principal Effectiveness
Our measures of principal eﬀectiveness encompass three distinct views of job perfor-
mance: ratings from supervisors, scores derived from student achievement data, and
assessments provided by teachers. First, TDOE provided us with principal evaluation
information from the Tennessee Educator Acceleration Model (TEAM) for the 2011–
12 through 2014–15 school years. TEAM is the statewide educator evaluation system
that TDOE created as part of its Race to the Top education reforms.12 For principals,
TEAM evaluations are composed of two portions, each accounting for 50 percent of
the ﬁnal evaluation score. The ﬁrst portion comes from supervisor ratings of principal

9. Our deﬁnition of exit at time t only makes use of data from time t + 1, raising the concern that we label principals
as exiting when they leave only for a short time and later reenter the principalship, a relatively frequent phe-
nomenon for teachers (Grissom and Reininger 2012). However, pooling across all available years of turnover
data, less than 1 percent of principals coded as “exiters” later return to the principalship. Also, the data do not
record layoﬀs as a special category, so this category will contain principals who leave the Tennessee education
system as the result of a layoﬀ.

10. We do not distinguish between within-district and across-district position changes (i.e., demotions and promo-
tions) because of the very small number of across-district position changes. Speciﬁcally, 81 percent of promoted
principals and 83 percent of demoted principals remain in the same district.

11. Because of missing data regarding assignment in the staﬀ ﬁles, there are a small number of principals in each
year for whom we cannot reliably determine a turnover classiﬁcation. These principals are dropped from the
analysis.

12. A small number of Tennessee districts elected to use alternative evaluation systems. Because we have incom-
plete data on the principal practice ratings principals are given in those alternative systems, we exclude those
ratings from our main analyses. We reestimated the main models using data from all school systems in the
state, and the results were largely unaﬀected. In addition, a small number of principals in TEAM districts do
not have TEAM ratings. Some of these missing cases may be due to within-year turnover; the turnover rate is
30 percent for principals with missing cases, compared with 14.5 percent for non-missing cases. Overall, our
main analytic dataset represents approximately 80 percent of principal-year observations over the timeframe
of the data. Sixty percent of missing cases come from just three districts (Shelby, Hamilton, and Knox).

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Principal Effectiveness and Turnover

performance on a rubric derived from the Tennessee Instructional Leadership Stan-
dards.13 As of 2014–15, the rubric deﬁnes principal leadership across seventeen indica-
tors in four domains, such as Instructional Leadership for Continuous Improvement
and Culture for Teaching and Learning. Principal ratings are based on formal observa-
tions typically conducted by the principal’s supervisor, the superintendent, or another
central oﬃce leader. Because not all principals received mid-year observations in the
initial years of TEAM implementation, we use the end-of-year summative rating for all
principals. Prior research shows that principals’ scores across indicators are so highly
intercorrelated that they can be reduced to a single underlying performance score us-
ing factor analysis (Grissom, Blissett, and Mitani 2018). In this analysis, we take the
predicted score from this factor model to be the supervisor’s rubric-based assessment
of the principal’s eﬀectiveness; we refer to this measure as a principal’s TEAM rating.14
The second component of the evaluation system, which we also use as a poten-
tial eﬀectiveness measure, comes from value-added estimates calculated from stu-
dent achievement data collected via the Tennessee Value-Added Assessment System
(TVAAS). For 2012–13 through 2014–15, TDOE provided us with composite school-level
value-added scores from TVAAS, which combine performance across all tested class-
rooms, subjects, and tests administered in a given school into a single index. Essentially,
the TVAAS measure represents the diﬀerence between a school’s expected growth (per
the TVAAS formula) and actual growth on statewide standardized tests.

We also create a measure of principal eﬀectiveness from a statewide survey of teach-
ers conducted by researchers from 2011–12 to 2013–14 as part of the evaluation of re-
forms under Tennessee’s First to the Top (FTTT) legislation, which was associated with
the state’s Race to the Top award. Across years, teacher response rates ranged from 25
percent to 40 percent. As part of the FTTT Survey, teachers were randomly assigned to
respond to diﬀerent modules, one of which contained a battery of questions speciﬁcally
designed to assess their principal’s leadership.15 These items ask, for example, whether
the school’s principal consistently monitors student academic progress, communicates
a clear vision for the school, or sets high standards for teaching. Using factor analysis,
we again found that responses measured one underlying latent construct, which we
take to be teachers’ perceptions of principal eﬀectiveness. To obtain a principal-level score,
which we refer to as the principal’s FTTT score, we averaged the teacher-level factor
scores at each principal’s school.

In addition to capturing distinct aspects of principal performance, these measures
diﬀer in their usefulness to district leaders for informing principal retention and mobil-
ity decisions. The rubric scores used to construct the TEAM rating are the most readily
available, timely, and likely relevant measures, because those who assign the scores
(e.g., superintendents or principal supervisors) typically also oversee management and
placement of principals. School value-added measures are publicly available and valued
for accountability purposes by district and state leaders, but educators may discount

13. For more information about TEAM, see http://team-tn.org/evaluation/administrator-evaluation/.
14. The average across items is correlated with the factor score at 0.97.
15. Because response rates were relatively low and only a random subset of responding teachers received the lead-
ership module, 27 percent of principal-year observations are missing the FTTT score due to nonresponse for
the three school years in which the survey was implemented. For more information about the FTTT survey,
see https://peabody.vanderbilt.edu/TERA/Tennessee_Educator_Survey.php.

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their accuracy, and often they are not made available until late summer or during the
fall of the next school year, which may limit their usefulness to district leaders for reten-
tion decisions (see Goldring et al. 2015). The ﬁnal measure, constructed from teacher
survey responses, are researcher-collected and not observed by school districts, mak-
ing them not directly relevant to district decision making, though district leaders may
have access to proxies to teachers’ responses, such as feedback from local surveys or
informal reports from teachers in the principal’s school.

5 . M E T H O D S
Our ﬁrst research question is: To what extent are more eﬀective principals more or less
likely to turn over? For this question, we begin by examining bivariate relationships
between turnover and school and principal factors via simple t-tests for diﬀerences in
mean turnover rates by these characteristics. For the multivariate analysis, we run linear
probability models of the following form:

Pr (Principal turnover)i jt

(cid:2)

= f

Eit, Pit, Sit, α j, τt, εi jt

(cid:3)

(1)

Equation 1 models the probability that a principal leaves her position after this school
year as a function of principal eﬀectiveness E, a vector of principal characteristics P,
a vector of school characteristics S, a district ﬁxed eﬀect α j, a year ﬁxed eﬀect τt, and
a random error term ϵ. Some previous studies of principal turnover have utilized a
discrete-time hazard model by including binary indicators for each year a principal has
“survived” in her school; we instead control for a categorical measure of tenure, which
provides a more readily-interpretable set of coeﬃcients.16 We estimate equation 1 us-
ing ordinary least squares (OLS) for ease of interpretation and because OLS allows for
straightforward inclusion of district ﬁxed eﬀects to account for time-invariant character-
istics of school districts that may aﬀect both principal eﬀectiveness and the likelihood
of turnover. By isolating variation within school districts, we account for some poten-
tial sources of bias, such as more eﬀective principals selecting into districts with lower
average turnover rates.

Next, we ask: To what degree does the relationship between principal eﬀectiveness
and principal turnover change for diﬀerent types of principal turnover, including moves
to other schools/districts, attrition from the education system, promotions, and demo-
tions? For this question, we tweak equation 1 to correspond to the multinomial case, and
we predict the relative probability of falling into the set {stay, move within district, move
across district, exit the system, promoted to central oﬃce position, demoted to school-based
position} in the following year as a function of the same covariates used in equation 1.
Again to accommodate ﬁxed eﬀects, we use OLS rather than a multinomial model, such
as multinomial logit or probit, estimating separate models for each turnover category
relative to the same base outcome (stays as principal in current school).17 Because we
estimate models pooled across years, individual principals have multiple observations

16. How we operationalize length of spell has virtually no eﬀect on our estimates of the relationship between the

performance measures and turnover.

17. The results are qualitatively similar across all turnover types when using a multinomial logistic regression

model. These results are shown in online Appendix tables A.4 and A.5.

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in the dataset. To account for repeated observations of the same principal across years,
we cluster standard errors at the principal level for all models.

In online Appendix ﬁgure A.1, we show the percentage of districts and schools who
experienced at least one turnover event from the 2011–12 to 2014–15 school years. Nearly
all districts had at least one principal turn over during the time period of the data,
suggesting substantial variation for estimating models with district ﬁxed eﬀects. In
contrast, only approximately half of schools had at least one principal turn over during
this time period, suggesting a much smaller eﬀective sample would be used if we used
school ﬁxed eﬀects instead. This consideration becomes even more important when
looking at speciﬁc types of turnover. For example, 68 percent of districts had at least one
principal who was promoted to a central oﬃce position, compared with only 11 percent
of schools. For this reason—although later we estimate school ﬁxed eﬀects models as
a check on robustness—district ﬁxed eﬀects models are the focus of our analysis.

6 . PAT T E R N S I N P R I N C I PA L T U R N OV E R AC RO S S S C H O O L

A N D P R I N C I PA L C H A R AC T E R I S T I C S

We begin our analysis of principal turnover by examining bivariate relationships be-
tween yearly turnover and school and principal characteristics, including measures of
eﬀectiveness. Mean turnover rates by the values of these variables are shown in table 2,
with asterisks indicating the result of a two-sided t-test for the diﬀerence between the
mean turnover rate for each row and the ﬁrst row in each group.

School characteristics are shown to the left. The ﬁrst takeaway is that schools with
smaller numbers of traditionally disadvantaged students have lower principal turnover.
In particular, schools with the lowest percentages of students who are FRPL-eligible (0–
19 percent FRPL) have the lowest principal turnover rates (13 percent); turnover rates
in schools with a majority of FRPL students are much higher (about 18 percent). Simi-
larly, schools with the highest average achievement level have the lowest turnover, with
annual turnover rates in schools more than 1.5 SD above the mean achievement level
of only 12 percent, compared with 24 percent in schools more than 1.5 SD below the
mean.

For average school-level growth (TVAAS), the patterns are similar—low-growth
schools have signiﬁcantly higher principal turnover rates than high-growth schools.
Schools with the highest growth have turnover rates of only 8 percent, compared with
25 percent in the lowest-growth schools. We also see that middle school principals are
signiﬁcantly more likely to turn over than elementary school principals (19 percent ver-
sus 16 percent). There are no signiﬁcant diﬀerences by locale.

The right column of table 2 shows yearly principal turnover rates broken down
by various principal characteristics, including eﬀectiveness. Unsurprisingly, principals
aged 60 years and older, who are more likely to be eligible for retirement, have much
higher turnover rates than other principals (26 percent to 15 percent, respectively),
whereas there is no diﬀerence in turnover rates among the other age ranges. Princi-
pals in their ﬁrst year at a school (0 years tenure in school) are the least likely to turn
over (12 percent turnover rate), with turnover rates ranging from 17 percent to 19 per-
cent for other tenure lengths. Similarly, principals with no prior principal experience
(i.e., principals in their ﬁrst year as a principal) are signiﬁcantly less likely to turn over

366

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Table 2. Principal Turnover Rates by Selected Principal and School Characteristics

School Characteristics

Principal Characteristics

Percent FRPL-eligible students

Principal age

0—19

20—39

40—59

60—79

80—100

0.13

0.17

0.15
0.18*
0.19**

Under 40 years

40—49 years

50—59 years

60 years and above

0.16

0.15

0.15
0.26***

Achievement index

Principal tenure in school

Below −1.50
−1.50 to −0.50
−0.49 to 0.50
0.51 to 1.50

Above 1.50

0.24

0.19
0.17***
0.15***
0.12***

0 years

1 year

2 years

3—4 years

5—7 years
8+ years

0.12
0.16***
0.18***
0.19***
0.19***
0.18***

TVAAS 1-year index

Prior principal experience

Below −1.50
−1.50 to −0.50
−0.49 to 0.50
0.51 to 1.50

Above 1.50

School level

Elementary

Middle

High

Other

Urban

Suburban

Town

Rural

0.25
0.17***
0.16***
0.17***
0.08***

0.16
0.19**

0.16

0.17

0 years

1—2 years

3—4 years

5—7 years
8+ years

TEAM rating

Below −1.50
−1.50 to −0.50
−0.49 to 0.50
0.51 to 1.50

Above 1.50

Locale

FTTT score

0.18

0.16

0.15

0.18

Below −1.00
−1.00 to −0.01
0.00 to 1.00

Above 1.00

0.10
0.17***
0.18***
0.19***
0.18***

0.30
0.14***
0.13***
0.13***
0.16***

0.22

0.19
0.17**
0.15***

Notes: Asterisks indicate significance in a t-test for a difference in means between
that row and the first row in the category. FRPL = free or reduced-price lunch;
FTTT = First to the Top; TEAM = Tennessee Educator Acceleration Model; TVAAS =
Tennessee Value-Added Assessment System.
*p < 0.10; **p < 0.05; ***p < 0.01. than principals in other years. This ﬁnding stands somewhat in contrast to research on teacher turnover, which typically ﬁnds the highest turnover rates among ﬁrst-year teachers. The ﬁnal entries in table 2 show the yearly principal turnover rate across two mea- sures of principal eﬀectiveness: rubric-based ratings from supervisors (TEAM rating) and survey ratings from teachers working in the school (FTTT score18). Each measure is expressed in terms of standard deviations from the mean. In both cases, the turnover 18. The distribution of FTTT scores has a negative skew compared with TEAM and TVAAS, which is why we have fewer groups with smaller score ranges. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / / f e d u e d p a r t i c e - p d l f / / / / 1 4 3 3 5 5 1 6 9 3 0 2 9 e d p _ a _ 0 0 2 5 6 p d . f / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 367 Principal Effectiveness and Turnover rate is highest for the least eﬀective principals, providing initial descriptive evidence that higher-performing principals are less likely to turn over. In particular, 30 percent of principals with a TEAM rating below −1.50 leave their position each year, compared with only 16 percent of principals with a TEAM rating above 1.50 (p < 0.01). Likewise, 22 percent of principals with an FTTT score below −1 leave their position, compared with 15 percent of principals above 1 (p < 0.01). 7 . P R E D I C T I N G T H E P RO B A B I L I T Y O F T U R N OV E R The patterns in table 2 show that principals who receive lower eﬀectiveness rat- ings, regardless of the measure, are more likely to leave their positions. Addition- ally, principals in less advantaged school contexts (e.g., with lower levels of student achievement) are also more likely to turn over. These descriptive facts help frame the remainder of our analysis. First, similar to prior research, we investigate correlates of principal turnover without measures of eﬀectiveness. We then examine whether the associations between principal eﬀectiveness and turnover seen in table 2 hold once conditioning on the characteristics of the districts and schools in which they work. Fi- nally, we account for the complexity of principal career paths by diﬀerentiating among speciﬁc types of turnover and demonstrating how eﬀectiveness relates to these diﬀerent outcomes, conditional on district and school characteristics. Estimating a Baseline Turnover Model We begin our multivariate analysis by estimating versions of equation 1 without any of the eﬀectiveness measures to provide a comparison to earlier studies. Because the dependent variable is binary (turnover or stay) and the model is estimated via OLS; coeﬃcients represent the marginal change in the probability that a principal leaves his position in a given year associated with each variable. Table 3 shows the results of this baseline model. Column 1 includes principal char- acteristics, school characteristics, and district characteristics (for parsimony, not all coeﬃcients are shown19). Columns 2 through 4 use district ﬁxed eﬀects instead of dis- trict characteristics. These models include diﬀerent combinations of the school achieve- ment index and the percentage of students qualifying for FRPL, which are highly correlated (r = −0.75). Importantly, principal turnover is substantially lower in higher- achieving schools, regardless of whether or not we control for the fraction of students who are FRPL-eligible (columns 2 and 4). A 1-SD decrease in the achievement index increases the probability of principal turnover by 4.2 percentage points (p < 0.01), a policy-relevant ﬁnding in light of research suggesting that the negative eﬀects of principal turnover may be larger in schools with larger numbers of low-income and low-achieving students (Béteille, Kalogrides, and Loeb 2012; Wills 2016). Column 3 shows that when omitting the achievement index, principals working in schools serving large numbers of students qualifying for FRPL are more likely to leave their positions. 19. The table omits principal experience, the proportion of students with disabilities, the proportion of students classiﬁed as gifted, and all district characteristics. None of these coeﬃcients was statistically signiﬁcant in any speciﬁcations. The full results are shown in online Appendix table A.2. We also ran models that included district and principal characteristics only, as well as district and school characteristics only. These results are very similar to those in column 1. 368 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . f / / e d u e d p a r t i c e - p d l f / / / / 1 4 3 3 5 5 1 6 9 3 0 2 9 e d p _ a _ 0 0 2 5 6 p d . f / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Jason A. Grissom and Brendan Bartanen Table 3. Predicting the Probability of Principal Turnover (Base Model) (1) (2) (3) (4) Principal characteristics Total salary (log) Female Age (tens) Age (tens squared) Education specialist or doctorate Black Tenure in School 0 years 1 year 2 years 5—7 years 8+ years School characteristics Achievement index Enrollment (100s) Proportion black Proportion Hispanic/Latino Proportion FRPL Middle school High school Other school District fixed effects Observations R2 −0.044 (0.049) 0.006 (0.011) 0.022*** (0.007) 0.035*** (0.007) −0.005 (0.011) 0.009 (0.021) −0.038 (0.026) −0.056** (0.024) −0.016 (0.024) −0.003 (0.022) −0.049** (0.024) −0.045*** (0.010) −0.003** (0.002) −0.014 (0.051) −0.103 (0.088) −0.022 (0.052) 0.043*** (0.014) 0.019 (0.017) 0.028 (0.028) 4,838 0.038 −0.055 (0.072) 0.004 (0.011) 0.023*** (0.008) 0.033*** (0.006) −0.015 (0.012) 0.023 (0.020) −0.077*** (0.027) −0.079*** (0.023) −0.037 (0.023) −0.000 (0.021) −0.032 (0.024) −0.040*** (0.009) −0.003* (0.002) −0.027 (0.050) −0.118 (0.081) 0.051*** (0.015) 0.020 (0.018) 0.009 (0.030) √ 4,994 0.082 −0.065 (0.069) 0.008 (0.011) 0.021*** (0.007) 0.032*** (0.006) −0.018 (0.011) 0.022 (0.020) −0.078*** (0.026) −0.075*** (0.023) −0.035 (0.023) −0.004 (0.021) −0.039* (0.023) −0.002 (0.002) 0.006 (0.052) −0.105 (0.085) 0.087* (0.047) 0.053*** (0.015) 0.021 (0.018) −0.012 (0.025) √ 5,146 0.078 −0.055 (0.072) 0.006 (0.011) 0.022*** (0.008) 0.033*** (0.006) −0.014 (0.012) 0.026 (0.020) −0.084*** (0.027) −0.080*** (0.023) −0.038 (0.023) −0.002 (0.021) −0.034 (0.024) −0.042*** (0.011) −0.003 (0.002) −0.029 (0.055) −0.117 (0.091) −0.013 (0.058) 0.053*** (0.015) 0.021 (0.019) 0.009 (0.030) √ 4,949 0.082 Notes: Individual-level clustered standard errors in parentheses. The dependent variable is a binary indicator for whether a principal left his position in the following year. Models estimated via ordinary least squares. Models also control for prior principal experience, proportion of students with disabilities, and proportion classified as gifted. Column 1 controls for district characteristics. All models include year fixed effects. FRPL = free or reduced-price lunch. *p < 0.10; **p < 0.05; ***p < 0.01. However, once we control for achievement in column 4, there is no statistical relation- ship between student poverty and principal turnover. Across models, the estimated coeﬃcients for other factors are fairly stable. We focus our discussion on the preferred speciﬁcation in column 4, which contains full school l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / / f e d u e d p a r t i c e - p d l f / / / / 1 4 3 3 5 5 1 6 9 3 0 2 9 e d p _ a _ 0 0 2 5 6 p d . f / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 369 Principal Effectiveness and Turnover and principal characteristics, and includes district and year ﬁxed eﬀects. Among prin- cipal characteristics, age and tenure in the school are signiﬁcant predictors of turnover. Age (in tens of years) and its squared term are signiﬁcant and positive, indicating that older principals are more likely to leave their position, and that the relationship is non- linear; the association between age and turnover results primarily from high turnover levels among much older principals, which we would expect if retirement is a primary driver of turnover. This mirrors the ﬁnding in table 2 that principals aged 60 years and above leave their positions at signiﬁcantly higher rates than principals under the age of 60 years. We also ﬁnd that turnover is lowest at the beginning of a principal’s stint in leadership or in his school. Principals in their ﬁrst or second year at a school have a predicted turnover rate roughly 8 percentage points lower than principals with three or four years of experience in their school (p < 0.01). This pattern could reﬂect a general preference of district leaders to avoid churn in school leadership or principals’ commitments to remain in a new school for at least a few years. Column 4 ﬁnds no diﬀerences in turnover by principals’ gender, race, or level of education. Additionally, although the point estimate of total salary is consistently nega- tive (which would indicate that higher-paid principals are less likely to leave their posi- tions), the standard errors are large and the estimated relationships are not statistically signiﬁcant. One potential explanation for this null ﬁnding is that, conditional on other covariates and district ﬁxed eﬀects, there is insuﬃcient variation in principal salary to precisely estimate its relationship with turnover. Indeed, when estimating salary pre- diction models for individual districts, we ﬁnd that principal characteristics (e.g., years of experience, education level) and school level explain 90 percent of the variation in principal salaries, on average. Among school characteristics other than achievement and student poverty, school level stands out. Compared with elementary principals, the predicted probability of turnover among middle school principals is 5.3 percent- age points higher (p < 0.01), with no signiﬁcant diﬀerences for high school or “other category” principals. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / f / e d u e d p a r t i c e - p d l f / / / / 1 4 3 3 5 5 1 6 9 3 0 2 9 e d p _ a _ 0 0 2 5 6 p d . / f Principal Effectiveness and Turnover Next, we consider whether principal eﬀectiveness predicts turnover, accounting for other individual, school, and district factors. Table 4 shows the results of models that include principals’ subjective evaluation ratings from their supervisors (TEAM). Be- fore turning to the main results, we note that patterns for the principal covariates are very similar to those shown in table 3; we have omitted them for brevity.20 Addition- ally, even conditional on eﬀectiveness, middle school principals have higher average turnover rates.21 f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 20. See online Appendix table A.3 for full results. Age and tenure in school remain signiﬁcant predictors of prin- cipal turnover, even after accounting for principal eﬀectiveness. The coeﬃcient on total salary is similar in magnitude to the base models and statistically insigniﬁcant. One potential concern with controlling for salary is that it may mediate the relationship between eﬀectiveness and turnover. However, models that omit salary produce nearly identical results as those in table 4. 21. Given the substantially higher turnover rates for middle school principals, we ran separate models by school level to investigate whether elementary, middle, and high school principals were diﬀerentially responsive to evaluation ratings. We did not ﬁnd substantively meaningful diﬀerences in the TEAM rating coeﬃcients across these models. 370 Jason A. Grissom and Brendan Bartanen Table 4. The Association between Principal Effectiveness (TEAM) and Principal Turnover (1) (2) (3) (4) (5) Principal effectiveness TEAM rating TEAM rating (squared) TEAM rating below −1.50 TEAM rating −1.50 to −0.51 TEAM rating 0.51 to 1.50 TEAM rating above 1.50 School characteristics Achievement index Enrollment (100s) Proportion black Proportion Hispanic/Latino Proportion FRPL Middle school High school Other school Principal characteristics District fixed effects Observations R2 −0.024*** (0.008) −0.020** (0.008) −0.011 (0.007) 0.041*** (0.005) −0.038*** (0.011) −0.003 (0.002) −0.022 (0.055) −0.114 (0.091) −0.015 (0.058) 0.050*** (0.015) 0.021 (0.019) 0.009 (0.030) √ √ 4949 0.084 −0.041*** (0.011) −0.003* (0.002) −0.029 (0.054) −0.112 (0.090) −0.020 (0.056) 0.050*** (0.015) 0.024 (0.018) 0.005 (0.030) √ √ 4949 0.098 −0.002 (0.002) 0.008 (0.052) −0.108 (0.085) 0.072 (0.047) 0.049*** (0.015) 0.020 (0.018) −0.013 (0.025) √ √ 5146 0.081 0.170*** (0.031) 0.021 (0.013) 0.018 (0.013) 0.058** (0.025) −0.002 (0.002) 0.004 (0.052) −0.120 (0.086) 0.082* (0.047) 0.051*** (0.015) 0.021 (0.018) −0.011 (0.024) √ √ 5146 0.087 0.178*** (0.031) 0.017 (0.014) 0.025* (0.014) 0.070*** (0.026) −0.040*** (0.011) −0.003* (0.002) −0.025 (0.055) −0.125 (0.090) −0.013 (0.057) 0.051*** (0.015) 0.022 (0.018) 0.009 (0.029) √ √ 4949 0.093 Notes: Individual-level clustered standard errors in parentheses. The dependent variable is a binary indicator for whether a principal left her position in the following year. Models estimated via ordinary least squares. In addition to principal characteristics and district fixed effects, models also control for proportion of students in the school with disabilities and proportion classified as gifted. All models include year fixed effects. FRPL = free or reduced-price lunch; TEAM = Tennessee Educator Acceleration Model. In columns 4 and 5, the omitted category for TEAM rating is −0.50 to 0.50. *p < 0.10, **p < 0.05, ***p < 0.01. Column 1 includes the TEAM rating, ﬁrst without controlling for the school’s achievement index. The estimated coeﬃcient shows a negative relationship between eﬀectiveness and turnover. A 1-SD increase in TEAM rating predicts a 2.4 percentage point (14 percent of the base turnover rate) decrease in the probability of turnover (p < 0.01)—more eﬀective principals are less likely to leave their positions. One potential concern is that principals’ TEAM ratings proxy for the average achievement level of the school, rather than an underlying construct of principal ef- fectiveness. While the TEAM rating and achievement index are positively correlated (r = 0.29), column 2 shows that adding the achievement index to the model does not greatly aﬀect the estimated association between TEAM rating and principal turnover. The TEAM rating coeﬃcient drops slightly (−0.024 to −0.020), but remains l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . f / / e d u e d p a r t i c e - p d l f / / / / 1 4 3 3 5 5 1 6 9 3 0 2 9 e d p _ a _ 0 0 2 5 6 p d f / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 371 Principal Effectiveness and Turnover Notes: Figure reflects the estimates from table 4, column 3. Shaded region indicates the 95% confidence interval. Figure 1. TEAM Ratings Predict Principal Turnover statistically signiﬁcant (p < 0.05), suggesting that a school’s average level of achieve- ment explains only a portion of the association between a principal’s TEAM rating and their likelihood of turnover. Similarly, school achievement remains an important pre- dictor of turnover, meaning that diﬀerences in principal turnover by school achieve- ment are not fully accounted for by principal eﬀectiveness. Column 3 tests for a nonlinear relationship between eﬀectiveness and turnover by including a squared term of the TEAM rating. This squared term is large, posi- tive, and statistically signiﬁcant, and improves model ﬁt in comparison to column 2. Figure 1 graphs this nonlinear association, showing that principals with performance scores close to the average are the least likely to leave their positions, whereas both low- performing and high-performing principals are more likely to leave, though the highest turnover rates are among the lowest performers. Prior work has documented a similar U-shaped relationship for teachers. For example, Feng and Sass (2017) ﬁnd that Florida teachers in the highest and lowest quartiles of value added are more likely to leave the public school system than average value-added teachers. To conﬁrm this relationship, we reestimate the model using categorical indicators for TEAM rating, with and without controlling for the school’s achievement index. The results in columns 4 and 5 map clearly onto the pattern in ﬁgure 1. In the full model, very low-performing principals (TEAM rating below −1.50) have a predicted turnover rate 17.8 percentage points greater than average-performing principals (TEAM rating −0.49 to 0.50), and the highest performers (TEAM rating above 1.50) have rates that are 7.0 percentage points higher than for average principals. One explanation for the U-shaped relationship between eﬀectiveness and turnover is that the binary turnover outcome conﬂates diﬀerent kinds of career behavior, a possibility we investigate more fully below by estimating multinomial turnover models. 372 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / f / e d u e d p a r t i c e - p d l f / / / / 1 4 3 3 5 5 1 6 9 3 0 2 9 e d p _ a _ 0 0 2 5 6 p d . f / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Jason A. Grissom and Brendan Bartanen Table 5. First to the Top (FTTP) Scores, School Value Added, and Principal Turnover (1) (2) (3) (4) (5) (6) Principal effectiveness FTTT score FTTT score (squared) TVAAS 1-year index −0.020*** (0.007) −0.019*** (0.007) −0.022*** (0.008) −0.004 (0.005) −0.023*** (0.006) −0.023*** (0.007) −0.023*** (0.007) TVAAS 1-year index (squared) Achievement index Principal and school controls District fixed effects Observations R2 √ √ 3,528 0.081 √ √ √ √ √ √ 3,426 0.083 3,426 0.083 √ √ 4,161 0.089 √ √ √ 4,111 0.087 0.001 (0.004) √ √ √ 4,111 0.087 Notes: Individual-level clustered standard errors in parentheses. The dependent variable is a binary indicator for whether a principal left his position in the following year. Models estimated via ordinary least squares. All models include year fixed effects. TVAAS = Tennessee Value-Added Assessment System. ***p < 0.01. Next, we shift from TEAM ratings as measures of principal eﬀectiveness to FTTT scores, which are constructed from teachers’ survey responses regarding school leader- ship, and TVAAS scores, which are school-level value-added measures. Table 5 shows the focal coeﬃcients from the same model speciﬁcations as in table 4, replacing TEAM ratings with these measures. Column 1 shows a negative and statistically signiﬁcant relationship between FTTT scores and the binary measure of principal turnover. A 1-SD increase in a principal’s FTTT score is associated with a 2.0 percentage point decrease in the likelihood of turnover (p < 0.05). As with the TEAM results, controlling for the school’s achieve- ment index (column 2) does not explain much of the relationship between FTTT and turnover. Additionally, column 3 shows no evidence of a nonlinear relationship. Column 4 shows a negative relationship between TVAAS and turnover. A 1-SD in- crease in the school’s TVAAS score predicts a 2.3 percentage point decrease in the prob- ability that the principal leaves his position (p < 0.01). Again, controlling for the school’s average level of student achievement leaves the magnitude of the TVAAS coeﬃcient un- aﬀected. Also, as with FTTT, there is no evidence of a nonlinear relationship between TVAAS and principal turnover.22 Together, these results provide consistent evidence of a relationship between a prin- cipal’s eﬀectiveness and his likelihood of turnover. As Appendix table A.4 shows, these 22. Because of concerns that TVAAS fails to separate principal impacts on test score growth from the impact of the school overall, we also explored models that substitute a measure of principal value added in these models, measuring principal value added as the coeﬃcient on principal ﬁxed eﬀects for each principal in a student growth model that also includes school ﬁxed eﬀects. However, separating principal eﬀects from school eﬀects in this way requires principals to work in multiple schools, which presents a selection problem from associating these measures with turnover. Speciﬁcally, because of the relatively short data frame, principal value added can only be estimated for the subset of principals who experience a turnover event. Because these principals tend to be lower performing on average, models that include principal value added systemically exclude higher- performing principals. Because of these limitations, we chose not to include these results. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / f / e d u e d p a r t i c e - p d l f / / / / 1 4 3 3 5 5 1 6 9 3 0 2 9 e d p _ a _ 0 0 2 5 6 p d . f / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 373 Principal Effectiveness and Turnover Table 6. Relative Predictive Power of Different Measures of Principal Effectiveness (1) (2) (3) (4) Principal Effectiveness TEAM Rating FTTT Score TVAAS 1-Year Index −0.022** (0.011) −0.018** (0.007) Observations R2 2894 0.095 −0.028*** (0.010) −0.011 (0.007) 3439 0.099 −0.031** (0.014) −0.019** (0.009) 0.002 (0.010) 1922 0.125 −0.015* (0.008) −0.008 (0.010) 2217 0.120 Notes: Individual-level clustered standard errors in parentheses. All models include year and district fixed effects, and the full set of principal and school controls. The dependent variable is a binary indicator for whether a principal left her position in the following year. Models estimated via ordinary least squares. TEAM = Tennessee Educator Acceleration Model; FTTT = First to the Top; TVAAS = Tennessee Value- Added Assessment System. *p < 0.10; **p < 0.05; ***p < 0.01. three eﬀectiveness measures are positively but not highly intercorrelated, suggesting that each captures a somewhat diﬀerent aspect of principal job performance. To more directly examine this possibility, we estimate a series of models that include two or more of the eﬀectiveness measures simultaneously. Table 6 shows these results. Col- umn 1 demonstrates that even conditional on one another, higher TEAM ratings and FTTT scores both remain associated with a lower probability of principal turnover. One interpretation of this result is that, even conditional on teacher ratings of eﬀectiveness, supervisors’ ratings predict turnover, suggesting that oﬃcial evaluation ratings inform principal career moves beyond what we would expect if we included only the kinds of survey-based measures used in some prior research (e.g., Sun and Ni 2016).23 Column 2, which replaces FTTT with TVAAS, yields the opposite result: Controlling for a prin- cipal’s eﬀectiveness in the form of supervisor ratings, school-level value added is not predictive of principal turnover, which we might expect if supervisor ratings implic- itly incorporate information on school test score growth (Grissom, Blissett, and Mitani 2017). Similarly, controlling for FTTT scores (column 3) attenuates the relationship be- tween TVAAS and turnover such that TVAAS is no longer statistically signiﬁcant. Once controlling for both TEAM rating and FTTT score (column 4), there is eﬀectively no re- lationship between TVAAS and turnover, while both TEAM and FTTT remain negative and statistically signiﬁcant. To guard against the possibility that unobserved school-level heterogeneity may bias estimates of the association between principal eﬀectiveness and turnover, we also es- timated models with school ﬁxed eﬀects. The disadvantage of these models is that they are estimated only from schools that have at least one turnover event over a short time period. Appendix table A.5 shows the results. The estimated relationship between 23. We also explored combining the TEAM and FTTT measures by creating groups of principals for each com- bination of high (above 75th percentile), medium (25th to 75th percentile), and low (below 25th percentile) ratings. These results were consistent with the patterns in table 6. For example, the lowest-scoring principals (i.e., below 25th percentile in TEAM and FTTT) were the most likely to leave their positions. These results are available upon request. 374 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / / f e d u e d p a r t i c e - p d l f / / / / 1 4 3 3 5 5 1 6 9 3 0 2 9 e d p _ a _ 0 0 2 5 6 p d f / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Jason A. Grissom and Brendan Bartanen TEAM and turnover is similar to the district ﬁxed eﬀects estimate, though larger stan- dard errors associated with the smaller eﬀective number of observations means that the uptick in turnover among very eﬀective principals is no longer statistically signiﬁcant at conventional levels. We conclude that there is little evidence our main models are biased by school-level heterogeneity within school districts. 8 . M U LT I N O M I A L R E S U LT S In our analysis using a binary turnover outcome, we ﬁnd that both very high- performing and very low-performing principals (as measured by TEAM ratings) are more likely to leave their positions. A potential explanation for this ﬁnding is that a binary measure of turnover masks patterns between eﬀectiveness and speciﬁc types of turnover; turnover among high-performers and low-performers may be associated with diﬀerent paths out of the principalship. Additionally, diﬀerent eﬀectiveness measures may be more highly predictive of speciﬁc types of turnover outcomes. To better understand these dynamics, we estimate models that diﬀerentiate among types of turnover: moving across districts, moving to a diﬀerent school in the same district, promotion to a central oﬃce position, demotion to a school-based position, and exiting the education system.24 Table 7 (panel A) displays the results of our multinomial linear probability models using the TEAM measure.25 For each model, the base category is a principal staying in her school. Coeﬃcients estimate the marginal change in the probability of the given turnover category (e.g., exiting the system in column 1) relative to staying in her school. In table 4, we showed that principals with the highest and lowest TEAM ratings were more likely to leave their positions. Here, we see that the increased probability of turnover among low performers is split between principals who exit the education system, principals who are demoted to school-level positions, and, to a lesser extent, principals who move to a principal position in a diﬀerent district. In comparison with those of average performance, principals with TEAM ratings below −1.50 are 9.5 percentage points more likely to exit the education system (p < 0.01), 11.4 percentage points more likely to be demoted (p < 0.01), and 2.5 percentage points more likely to move to another district (p < 0.05).26 These patterns are consistent with school districts identifying and removing low performers from school leadership positions in their systems, though the high rates of exits and across-district moves may also reﬂect voluntary decisions to leave among principals who receive low ratings. For high-performing principals, the somewhat increased likelihood of turnover in the binary turnover model is explained by an increased likelihood of promotion to a cen- tral oﬃce leadership position and a higher likelihood of leaving the education system. 24. Online Appendix tables A.6 and A.7 show base multinomial models without district ﬁxed eﬀects, with and without the principal eﬀectiveness measures. 25. For brevity, we omit the principal and school characteristics from the table. The full results are shown in Ap- pendix table A.8. Unsurprisingly, age predicts exits (presumably due to retirement) and a slightly decreased likelihood of moving within or across districts. Except for an increased likelihood among female principals to exit the education system, we ﬁnd no strong evidence of associations between speciﬁc types of turnover and a principal’s gender, race, or level of education. Among school characteristics, the strong relationship between achievement index and the binary turnover measure is explained by patterns of exits and demotions. Even after controlling for eﬀectiveness, we ﬁnd that principals working in schools with low average achievement are more likely to leave the education system or to be demoted from their position. 26. Across-district moves for principals in Tennessee are relatively rare (approximately ten per year), so caution is warranted in interpreting these results. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / / f e d u e d p a r t i c e - p d l f / / / / 1 4 3 3 5 5 1 6 9 3 0 2 9 e d p _ a _ 0 0 2 5 6 p d / . f f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 375 Principal Effectiveness and Turnover Table 7. Multinomial Results Predicting Principal Career Paths with TEAM Ratings Panel A: TEAM Exit Promoted Demoted Within-District Across-District TEAM rating below −1.50 TEAM rating −1.50 to −0.51 TEAM rating 0.51 to 1.50 TEAM rating above 1.50 Observations R2 Panel B: FTTT FTTT score Observations R2 Panel C: TVAAS TVAAS 1-year index Observations R2 0.095*** (0.027) 0.006 (0.010) 0.014 (0.010) 0.043** (0.021) 4,527 0.104 0.006 (0.012) 0.001 (0.007) 0.013* (0.007) 0.030** (0.015) 4,359 0.056 0.114*** (0.025) 0.016** (0.007) 0.002 (0.006) −0.004 (0.006) 4,343 0.077 0.008 (0.019) −0.005 (0.008) 0.008 (0.008) 0.011 (0.014) 4,395 0.066 0.025** (0.012) 0.004 (0.003) −0.003 (0.003) 0.010 (0.007) 4,257 0.067 Exit Promoted Demoted Within-District Across-District −0.012** (0.005) 3,054 0.110 0.002 (0.004) 2,934 0.074 −0.015*** (0.004) 2,909 0.098 0.000 (0.004) 2,948 0.073 −0.002 (0.002) 2,838 0.078 Exit Promoted Demoted Within-District Across-District −0.014*** (0.005) −0.003 (0.004) −0.008** (0.004) 3,715 0.103 3,589 0.078 3,574 0.074 −0.001 (0.004) 3,562 0.067 −0.002 (0.002) 3,475 0.092 Notes: Individual-level clustered standard errors in parentheses. All models include year and district fixed effects, and the full set of principal and school controls. Each model estimated in reference to principals who remained in their position in the following year. Models estimated via ordinary least squares. TEAM = Tennessee Educator Acceleration Model; FTTT = First to the Top; TVAAS = Tennessee Value-Added Assessment System. In Panel A, the omitted category for TEAM rating is −0.50 to 0.50. *p < 0.10; **p < 0.05; ***p < 0.01. Compared with average-scoring principals, principals with TEAM ratings above 1.50 are 3 percentage points more likely to be promoted (p < 0.05), with a 1.3 percentage point increase for principals scoring 0.51 to 1.50 (p < 0.10). Similarly, these high-scoring prin- cipals are 4.3 percentage points more likely to leave the education system (p < 0.05), which could reﬂect opportunities for high performers in leadership in the private school sector or outside education, neither of which we can observe in our data.27 Panels B and C show the results of multinomial models for FTTT and TVAAS. Similar to TEAM, increased FTTT and TVAAS scores are associated with a decreased probability of demotion and exit. However, we ﬁnd no evidence of an association with the probability of promotion or across-district transfer.28 27. We also investigated interactions between the TEAM rating and whether the principal is female or black to assess whether the role of eﬀectiveness in turnover may vary for these groups due to bias or other factors. In general, we did not ﬁnd consistent evidence of important diﬀerences. Results for gender reveal few diﬀerences for men and women, with the exception that highly rated women may be less likely to move within the district while highly rated men may be more likely. Results for race suggest that the greater propensity for highly rated principals to be promoted is concentrated among white principals. These results are available upon request. 28. We also estimated multinomial models with school ﬁxed eﬀects (see online Appendix table A.9). Results for TEAM (panel A) are qualitatively similar to the district ﬁxed eﬀects results. The main diﬀerence is that there is no longer a statistically signiﬁcant relationship between high TEAM ratings and promotion to central oﬃce. For FTTT and TVAAS, there is attenuation of the coeﬃcients—perhaps reﬂecting a substantial ﬁxed school-level component of these measures—and nearly a twofold increase in the standard errors. None of the coeﬃcients is statistically signiﬁcant. 376 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . f / / e d u e d p a r t i c e - p d l f / / / / 1 4 3 3 5 5 1 6 9 3 0 2 9 e d p _ a _ 0 0 2 5 6 p d f . / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Jason A. Grissom and Brendan Bartanen 9 . D I S C U S S I O N A N D C O N C L U S I O N S Leveraging longitudinal administrative data from Tennessee, this study makes sev- eral contributions to our understanding of principal turnover. First, we demonstrate the importance of examining pathways out of the principalship. Exits from Tennessee schools altogether are about 40 percent of turnover, and we ﬁnd that many exiters are older, more experienced principals, who presumably are exiting for retirement. The remaining 60 percent of turnover cases are moves of leaders within the education sys- tem. Among these moves, we show that within-district transfers, promotions to cen- tral oﬃce, and demotions are roughly equally likely; nearly all of the latter two kinds of moves occur within school districts, suggesting that adjusting school districts’ de- cisions about personnel placement can have a great impact on principal turnover. In particular, we show that demotions to other school-level positions—which prior studies have ignored—constitute nearly one ﬁfth of principal turnover in our sample. Second, we replicate the ﬁnding from some prior studies that turnover is higher in schools with larger numbers of low-income students and lower average achievement. We also show that turnover is particularly high among middle school principals. Policy makers seeking to reduce principal turnover might focus attention speciﬁcally on these school contexts. Third, utilizing multiple measures of principal eﬀectiveness, we supply the new ﬁnding that principal job performance predicts turnover. In the binary case, we ﬁnd that low-performing principals are more likely to leave their current positions. However, in examining supervisors’ evaluation ratings, we also ﬁnd higher turnover among the highest performers. Examining diﬀerent pathways out of a principal’s position helps ex- plain this ﬁnding. Low-performing principals (across measures) are substantially more likely to be demoted to a lower school-level position (e.g., assistant principal) or leave the education system entirely, whereas high-scoring principals are more likely to be promoted to a central oﬃce position. The fact that demotions and exits are concen- trated among low performers points toward greater care in hiring eﬀective principals as a useful strategy for stemming principal turnover in subsequent years. The ﬁnding that higher-rated principals are more likely to be promoted to central oﬃce suggests that district leaders should consider the potential costs of moving eﬀective leaders out of schools alongside the (presumed) beneﬁts of having those leaders assume district leadership positions. Our results point to a number of additional areas for inquiry. Among principals who are demoted, for example, what are the future trajectories in job opportunities and job performance? What factors drive principal turnover in low-achieving schools and middle schools? What mechanisms link job performance with principal turnover outcomes? There are several potential answers to this last question, and they likely vary by pathway out. For instance, demotions likely are involuntary—that is, we expect that in- eﬀective principals are moved into other school-level positions by district oﬃce leaders to make way for higher performers. How job performance drives other job outcomes is less clear. For example, when we show that principals who receive low TEAM ratings are substantially more likely to leave the education system, we do not know whether they are forced out by the district (e.g., through contract nonrenewal) or whether they choose to leave (e.g., to retire or pursue another career) because they are unhappy in a l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / f / e d u e d p a r t i c e - p d l f / / / / 1 4 3 3 5 5 1 6 9 3 0 2 9 e d p _ a _ 0 0 2 5 6 p d f / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 377 Principal Effectiveness and Turnover job in which they are ineﬀective. Closer examination of these processes qualitatively or with additional administrative data would be valuable. On this latter hypothesis that some ineﬀective principals may leave because ineﬀec- tiveness correlates with less enjoyment of the job, we can provide some partial evidence. As part of the FTTT Survey, principals responded to a battery of questions about their job satisfaction. Using factor analysis, we identiﬁed a single factor from these items, which we used to create predicted scores for each principal, then standardized.29 We regressed this measure on the principal eﬀectiveness measures (TEAM rating, FTTT score, and TVAAS), with a full set of covariates and district and year ﬁxed eﬀects. We ﬁnd that both higher TEAM ratings and FTTT scores predict signiﬁcantly higher job satisfaction; TVAAS scores are not correlated (Appendix table A.10). Next, we estimate turnover models that include diﬀerent combinations of eﬀectiveness and job satisfac- tion to explore job satisfaction as a mediator of the eﬀectiveness–turnover relationship (Baron and Kenny 1986). The results for binomial and multinomial models are dis- played in Appendix table A.11. Unsurprisingly, the results show that increased principal satisfaction predicts a lower probability of turnover; a 1-SD increase in satisfaction is associated with a 4.8 percentage point decrease in the likelihood that a principal leaves her position (panel A, column 1). The remaining columns in panel A suggest that sat- isfaction may partially mediate the relationship between principal eﬀectiveness and turnover.30 Panels B through F show parallel results for multinomial linear probabil- ity models.31 Looking across these panels, the only suggestive evidence that job satis- faction mediates the eﬀectiveness–turnover relationship is for principals who exit the education system; satisfaction does not appear to be a mediator for the other turnover pathways.32 An implication of the exits ﬁnding is that improving job satisfaction by addressing diﬃcult principal working conditions often found in high-turnover schools may promote leadership stability in such environments. Our ﬁndings are limited by our reliance on data from a single state, and one with a particularly strong principal evaluation context, which may limit the generalizability of our results. In addition, although we provide suggestive evidence through our analysis of demotions and our supplemental investigation of principal job satisfaction, agency in principals’ turnover decisions largely remains a black box. Whether a principal leaves his or her position can be a function of principal preferences, district preferences, or a combination of both. Studies with more detailed information about principals’ work preferences from job applications, for example, may allow researchers to further dis- entangle these factors. More generally, the large unexplained variation in our turnover models highlights how much more remains to be learned about the drivers of principal turnover. Future 29. A limitation of the FTTT data is the low response rate for principals. The response rate was 25 percent, 38 percent, and 41 percent in 2011–2, 2012–13, and 2013–14, respectively. 30. The magnitude of the TEAM and FTTT coeﬃcients decreases with the inclusion of the satisfaction factor, and the prior table showed the strong association between TEAM/FTTT and job satisfaction. 31. Results for panels C and E correspond to earlier ﬁndings, but only for the principals with satisfaction data. 32. Panel B shows that job satisfaction is signiﬁcantly associated with decreased likelihood of exit, demotion, and within-district transfer. Using both TEAM and FTTT, there are small decreases in the magnitude of the ef- fectiveness coeﬃcient in the exits models when controlling for a principal’s self-reported job satisfaction. In contrast, including satisfaction does not aﬀect the relationship between TEAM/FTTT and other turnover measures. 378 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . f / / e d u e d p a r t i c e - p d l f / / / / 1 4 3 3 5 5 1 6 9 3 0 2 9 e d p _ a _ 0 0 2 5 6 p d . / f f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 Jason A. Grissom and Brendan Bartanen studies might explore the role of principal job attitudes and more direct measures of principal working conditions, for example, for which student demographics and other basic school characteristics may serve as poor proxies. Still, some results here are potentially promising. School districts are relatively suc- cessful at turning over low-performing principals, on average, and generally are not moving them to other schools in the same system. To the extent that they can replace those low performers with more eﬀective leaders, these higher turnover rates may ben- eﬁt school performance. Future work might investigate this possibility directly by link- ing turnover to school performance in future years (see Miller 2013) but diﬀerentiating by principal eﬀectiveness. It might also investigate whether the impact of principal turnover on student outcomes depends on the type of turnover. Exploring pathways in and out of assistant principal positions by eﬀectiveness measures would be another useful extension of this work. From a policy standpoint, higher rates of turnover in low-achieving and high-poverty schools highlight the apparent need for interventions to curb administrator turnover in these schools. A consistent pattern has arisen in the literature that student characteris- tics are among the most important predictors of principal turnover. Given research link- ing leadership turnover to negative impacts on student performance, policy attention to strategies aimed at keeping eﬀective principals in high-need environments may yield large dividends. Salary increases for eﬀective principals are one obvious potential pol- icy lever. In exploratory analysis not shown, we ﬁnd that higher-performing principals who stay in their schools receive slightly higher salaries the next year than less eﬀective principals, suggesting that some Tennessee schools may be using strategic compensa- tion to reward or retain eﬀective leaders. Because we cannot observe the salaries (or oﬀered salaries) of those who exit the education system, we cannot speak directly to whether oﬀering bonuses to high performers induces lower rates of exit. Further anal- ysis of strategic compensation of school leaders and impacts on school leader turnover would be valuable for both research and policy. ACKNOWLEDGMENTS We thank the Tennessee Education Research Alliance and the Tennessee Department of Educa- tion for facilitating this research. REFERENCES Baker, Bruce D., Eric Punswick, and Charles Belt. 2010. 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