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Are there hidden costs associated with teacher layoffs? The impact of job insecurity on teacher effectiveness in the Los Angeles Unified School District
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Content
ARE THERE HIDDEN COSTS ASSOCIATED WITH TEACHER LAYOFFS?
THE IMPACT OF JOB INSECURITY ON TEACHER EFFECTIVENESS IN THE
LOS ANGELES UNIFIED SCHOOL DISTRICT
by
David S. Knight
A Thesis Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF ARTS
(ECONOMICS)
August 2015
Copyright 2015 David S. Knight
2
Table of Contents
Body text 3
Tables 29
References 34
Appendix 41
3
Are There Hidden Costs Associated with Teacher Layoffs?:
The Impact of Job Insecurity on Teacher Effectiveness in the
Los Angeles Unified School District
“Even if the odds are good that a teacher will be rehired, these pink slips take their toll on staff
morale and job satisfaction. It can strain staff relationships, affect morale, and cause stress and
anxiety among those threatened with joblessness during a period of high unemployment.” –Frey,
2012
One of the consequences of the Great Recession of 2008 was a series of massive funding
cuts to public education in the United States, forcing school districts around the country to
reduce budgets and conduct layoffs. Estimates of teacher layoffs stemming from the Great
Recession range from 170,000 to 240,000 by the 2011-12 school year (Bureau of Labor
Statistics, 2012; National Education Association, 2010).
1
In many districts, and especially in
large urban centers such as Los Angeles, New York City, and Chicago, budget shortfalls
necessitated particularly deep reductions in the labor force. For example, the Los Angeles
Unified School District laid off over 4,600 teachers over a four-year period, from 2008-09
through 2011-12.
2
Scholars are beginning to examine the effects of these layoffs by studying their impacts
on teacher mobility patterns and changes in the composition of the teaching workforce (Boyd,
Lankford, Loeb, & Wyckoff, 2011; Kraft, 2015). Research suggests that teacher layoff policies
typically rely primarily on seniority (Last-In-First-Out, or LIFO), causing high-performing junior
faculty to be targeted for layoffs and removed from the classroom while potentially lower
1
Note that the lower number, provided by the National Education Association is based on the layoffs that took place
from 2008 to 2010, and is likely a lower bound estimate of the total number of teachers laid off since the Great
Recession.
2
Based on Author’s calculations using LAUSD administrative teacher data. This exact number of layoff notices
distributed to teachers depends on how classroom teachers are defined. I define teachers as any employees listed in
the non-administrator demographic file with the job title of teacher that can be linked to a single school. In my main
analyses, I exclude itinerant teachers, returned retirees, and long-terms substitutes, but include these employees in
specification checks.
4
performing senior faculty are retained (Goldhaber & Theobald, 2013). Some scholars argue that
students will suffer because of the distributional shift in teacher effectiveness caused by LIFO
layoff policies.
However, what has been less reported in the media, and less studied by researchers
attempting to understand the impacts of layoffs on the education workforce, is the impact of the
broader layoff process. In particular, the way layoffs are conducted in most districts around the
country requires districts to send initial layoff warning notices – called Reduction-in-Force (RIF)
notices – to a far greater number of teachers than will actually need to be let go to meet budget
projections. In California, for example, state legislation requires public school districts to send
RIF notices by March 15 to any employee who may conceivably be let go for the following
school year. Then, California districts must send final layoff notices by May 15. However, state
budgets – and therefore district budgets – are often not finalized until June or later, requiring that
school districts send out notification of RIFs and layoffs in the absence of perfect information
about their budget for the coming year. Over the four years of layoffs, 14,142 RIF notices were
distributed (13.3% of teachers each year) and 68.6% of those were rescinded. Similarly, while
4,445, layoff notices that were distributed, the teacher was rehired the following year in 48.1% of
those cases. These figures are emblematic of the situations in districts across the nation.
Washington State rehired 84.1% of the 3,538 teacher layoffs over the same time period and
Charlotte-Mecklenberg school district rehired 33.0% of laid off teachers in 2008-09 and 2009-10
(Goldhaber et al., 2015; Kraft, 2015). In sum, the layoff process results in many more teachers
receiving RIF notices than will eventually be laid off, and in turn, many more teachers being laid
off then will eventually need to be removed from the district in order to meet budget realities.
5
The end result of the layoff process is that many teachers face substantial job threat but do not
eventually lose their jobs.
There has been little attention paid to the impacts of job uncertainty created by this layoff
process on teachers or on their students. However, there is reason to believe that the layoff
process might heavily impact teachers, even without considering the direct impact of layoffs on
teachers’ job loss. Recent research by Goldhaber et al. (2015) finds that the layoff process
discussed above causes teachers who received RIF notices but then had them rescinded to exit
their schools at significantly greater rates than teachers who were not impacted by the layoff
process. In addition, recent studies show that organizational context, which is likely harmed by
wide distribution of RIF notices and associated churn, plays a significant role in shaping
teachers’ impact on students (Hannaway, Sass, Figlio & Feng, 2009; Kraft & Papay, 2014).
More directly, there is a growing set of research that finds that teacher churn negatively impacts
student achievement (e.g., Guin, 2004; Ronfeldt, Loeb, & Wyckoff, 2014; Hanushek & Rivkin,
2013). Finally, literature from the field of psychology and behavioral economics suggests that
public sector workers may be more risk averse, and in general, workers may be less motivated
and thus less productive when their jobs are threatened (e.g., Brockner, Davy & Carter, 1985;
Brockner, Grover, O’Malley, Reed & Glynn, 1993; Buurman, Delfgaauw, Dur, & Van den
Bossche, 2012). Together, this research suggests that employment threat and distressed
professional environment induced by the layoff process may have severe implications for the
teacher workforce that remains in the district, and as such, for their students. In particular, the
layoff process may harm teachers’ instructional effectiveness.
This study is the first that I know of to examine how teachers’ own job performance
changes as a result of exposure to layoffs and layoff threat. Using longitudinal teacher-level data
6
from the Los Angeles Unified School District (LAUSD) before, during, and after the period in
which teacher layoffs were implemented (2006-07 to 2007-08, 2008-09 through 2011-12, and
2012-13, respectively), I assess how teachers’ effectiveness (measured by value-added measures
of teachers’ contributions to their students’ test scores) changes in response to layoffs or layoff
threat in the current and previous years. I measure layoffs and layoff threat as losing one’s job
but then returning to the district the following school year, and as receiving a RIF notice that was
then rescinded before the end of the prior school year, respectively. I find that teachers who are
laid off, but return to the district in the proximate year are less effective, although this is not the
case for teachers who are RIFed but not let go in the previous year (the findings section and
accompanying table are included in Appendix B). I argue that these results are driven by
teachers’ job insecurity and uncertainty associated with the layoff process and I present evidence
to rule out alternate explanations including selection bias, regression to the mean of value-added
scores, or transitions to a new school, grade level, or teaching position caused by the layoff
process.
The results from this study are instructive for policymakers as they look to the future.
Although Great Recession-era layoffs have ceased, recessionary trends are cyclical, and layoffs
will occur again. LAUSD, for example, distributed RIF notices in March of 2015, despite
additional state funding provided through a recent tax initiative (Blume, 2015). For
policymakers, it is important to consider how policies that lead to more teachers being impacted
by the layoff process than are necessary might be changed. Possibilities include requiring that
school districts and states have more information about budgets before sending out RIF notices
or laying off personnel. States may also consider appropriating education funding one year in
advance to ameliorate district budget uncertainties, as is done in Alaska, Wyoming, and other
7
states (Tollesrup, 2014). In addition, given the impact of RIFs and layoffs on teacher
effectiveness, it stands to reason that policies that evaluate teachers based on their effectiveness
should consider organizational context and job security during times of stress and strife, such as
layoffs.
The remainder of the paper proceeds as follows. In Section II, I provide background on
the impact of layoffs, both on the structure of the teaching force and on the potential responses of
teachers to employment threat. Section III outlines the layoff process in LAUSD and explains
why the district is a particularly relevant context for a study of the impacts of the layoff process
on teacher effectiveness. In Section IV, I describe my data and the analytic methods used to
assess these data. Section V provides findings, and Sections VI discusses these results, offers
policy implications, and concludes.
Literature on the Impacts of Layoffs and the Layoff Process
As discussed above, there are several ways that the layoff process might impact teachers
and students. First, layoffs can directly impact the teacher workforce by removing laid off
teachers from their districts, thereby increasing class sizes. Second, LIFO policies may remove
promising early career teachers that may have been protected under other variants of layoff
policies. Third, the broader layoff process can indirectly impact the teacher workforce by causing
teachers to exit their schools at greater rates than they otherwise would have. Forth, the
employment threat introduced by the layoff process may impact individual teachers’
organizational commitment, productivity, and professional development activities during
summer months. Last, layoffs and the layoff process can adversely impact teachers’ collective
productivity by harming the professional environments in which they work. I briefly review the
literature addressing each of these mechanisms in turn.
8
The Impacts of Layoffs on Workforce Composition
Research examining the direct impacts of layoffs on the teaching workforce has grown in
the wake of the Great Recession. This work shows that layoffs based on seniority result in the
dismissal of teachers rated as highly effective through value-added measures (Boyd et al., 2011;
Goldhaber & Theobald, 2013; Kraft, 2015). For example, using data from New York City, Boyd
et al. (2011) run simulations to assess the effects on the quality of the teacher workforce of
achieving a five percent budget reduction through either implementing layoffs based on seniority
(LIFO) or on a measure of effectiveness (VAMs). They find unsurprisingly that teachers let go
under an effectiveness-based layoff system are significantly less effective than teachers who are
laid off under a LIFO policy. Boyd et al. (2011) find similar results when teachers’ measures of
effectiveness are based on observations of teacher practice. Similarly, Goldhaber and Theobald
(2013) find that given the assumption that value-added measures of teacher effectiveness are
unbiased and stable over time, the use of a value-added based layoff process, rather than a LIFO-
based process, in Washington State would have resulted in student achievement gains of
approximately 20 percent in both math and reading. The most recent study that examines the
implications of layoffs on collective teacher quality comes from the Charlotte Mecklenberg
School District, which allowed district administrators discretion over which teachers to lay off
(Kraft, 2015). Kraft shows that the lack of reliance on seniority in layoffs enabled the school
district to remove less effective teachers, on average, than would have been the case under a
LIFO policy. Moreover, Kraft finds that while layoffs decrease grade-level achievement
generally, students are adversely impacted by layoffs of more effective teachers: when schools
lose an effective teacher as opposed to an ineffective teacher (judged by either VAMs or
9
principal ratings), math achievement for students in the impacted grades drops by 0.05 to 0.11
standard deviations.
In addition to overall quality shifts in the workforce stemming from LIFO-based layoff
policies, seniority-driven layoffs also result in a greater proportion of teachers leaving the
workforce. In the direct sense, this occurs because junior teachers earn lower salaries than
veteran teachers, so the requirement that the most junior teachers are let go first in order to meet
budget targets necessitates a greater quantity of layoffs. Studies in both New York City and
Washington State empirically confirm this pattern (Boyd et al., 2011; Goldhaber & Theobald,
2013; Roza, 2009). In New York City, seniority-based layoffs lead to an estimated 25 percent
more teachers being let go than would have been under an effectiveness-based layoff policy
(Boyd et al., 2011). In Washington, districts that enacted layoffs could have reached the same
budgetary targets by laying off 10 percent fewer teachers if layoffs were implemented based on
effectiveness rather than seniority (Goldhaber & Theobald, 2013).
The Impacts of the Layoff Process on Teachers and Students
Although the work reviewed above is critical for understanding how layoffs impact the
quality and size of the workforce, none of these papers assesses the impacts of the layoff process
beyond what occurs as a result of actual layoffs. In particular, little attention has been paid to the
impact of “surviving” a layoff. In other words, how do teachers who are faced with the layoff-
induced threat of job loss react in terms of their mobility, and how does layoff threat impact
teachers’ effectiveness and student outcomes?
A recent paper by Goldhaber and colleagues (2015) assesses the first part of this
question. They ask how layoff threat impacts teachers’ propensities to exit their schools, and find
that in Washington State and LAUSD, teachers who receive a RIF notice are significantly more
10
likely to exit their schools. However, it is unclear if this mobility is caused by teachers’ voluntary
responses to layoff threat or by the structural requirements stemming from seniority-based
layoffs (i.e., teachers who are RIFed but not let go are shuffled around schools to fill vacancies
left by laid off teachers). The authors also assess teachers’ responses to layoff threat stemming
from teachers’ colleagues being laid off, and find that, in LAUSD, teachers with greater peer-
induced layoff threat are more likely to exit their schools. In addition, teachers who are protected
from layoff threat through varying policies in LAUSD and Washington are less likely to exit
their schools.
There is no work that I know of that directly assesses the impact of layoff threat on
teacher effectiveness and student achievement, although extant research suggests that there are
negative effects on teacher productivity. In particular, the fact that layoff threat induces teacher
churn may cause both students and teachers to perform poorly. Sepe and Roza (2010), in their
study of layoffs on low-income students of color, argue that teacher churn in schools impacted
by layoffs destroys established relationships between teachers and students, families and
teachers, and teachers and administrators. In addition, turnover destabilizes schools and
negatively impacts their learning environments. This finding follows a set of studies that shows
that teacher churn negatively impacts school climate and student achievement (e.g., Guin, 2004;
Ronfeldt, Loeb, & Wyckoff, 2013; Hanushek & Rivkin, 2013). For example, Guin conducts case
studies of the perceived impact of teacher turnover. Teachers and principals reported that high
rates of teacher turnover made it more difficult for staff to build relationships with students,
harmed teacher collaboration, and resulted in the loss of investments in teacher professional
development. Ronfeldt et al. (2013) study grade- and school-level teacher churn in New York
city schools and find that a 100 percent increase in turnover at the grade or school level lowers
11
student achievement by between .066 and .081 standard deviations, with more detrimental
effects for low-income student and students of color. Hanushek and Rivkin (2013) find that
teacher turnover is even more detrimental in a large Texas school district; their results suggested
that 1 percent increase in teacher turnover lowered achievement by 0.028 standard deviations.
Similarly, a number of studies have begun to document how poor working conditions
may negatively impact teacher productivity (e.g., Hannaway, Sass, Figlio & Feng, 2009; Kraft &
Papay, 2014). For example, Johnson, Kraft and Papay (2012) find that teachers exhibit lower
returns to experience in schools they perceive to have poor working conditions. Other studies
have documented that teachers prefer working in schools where they perceive a supportive
working environment and strong administrative support (Boyd et al., 2011; Ladd, 2011). In short,
this research suggests that, by causing churn and poor working environments, the layoff process
is likely detrimental to teacher productivity and student outcomes.
Literature from the field of psychology also suggests that teachers may react to
employment threat in ways that could diminish their effectiveness and, as a result, student
achievement. In particular, research has found that workers are less motivated and thus less
productive when their jobs are threatened (e.g., Allen, Freeman, Russell, Reizenstein & Rentz,
2001; Brockner, Grover, Reed, & DeWitt, 1992). For instance, Brockner, Davy and Carter
(1985) conduct psychological studies of simulated layoffs in lab settings and find that exposure
to layoff threat decreases motivation and self-esteem. Research participants reported high levels
of “worry” when they had already been exposed to past layoffs and when they perceived that
layoff processes were conducted “unfairly.” Using, surveys of workers, Brockner et al. (1993)
find that employees report the highest levels of motivation when they perceive moderate levels
12
of layoff-induced job threat, but report lower levels of motivation when faced with high levels of
job insecurity.
Altogether, the literature that can be applied to the research question at hand suggests that
teacher effectiveness may diminish when teachers are faced with layoff-induced job insecurity.
In addition, there are pragmatic reasons that go beyond the literature reviewed above that could
lead to decreases in teacher productivity stemming from RIFs and layoffs. In many cases, laid off
teachers did not receive indication that they would be offered a job the following school year
until just before the beginning of the year, or, in comes cases, several weeks into the year.
Consider that teachers typically spend at least some of their summer months preparing for the
next school year, attending district professional development, and embarking upon other useful
activities intended to improve their instruction in the following year. Teachers engage in these
activities with the comfort of knowing they will have a job when the following school year
begins. Teachers who believe that they do not have a job in the coming year may spend less time
in preparation and improvement activities, and may spend more time searching for new
employment. These activities likely do not contribute to improvements in teaching performance
in the following year.
If the productivity of teachers who have been RIFed or laid off does indeed diminish in
the year after they have received RIF and layoff notification, then policymakers will need to
consider how to amend current layoff policies and structures to decrease the harm caused to
teachers and their students by layoffs and the larger layoff process. In the next section, I describe
how policymakers can learn from the LAUSD context, in which layoffs – and more importantly
the layoff process – were implemented for four years as a result of the Great Recession.
13
The Layoff Process in LAUSD: 2008-09 through 2011-12
LAUSD, like other school districts, was forced to implement layoffs at a scale greater
than any in recent memory. Table 1 shows the impact of the layoff process in LAUSD across the
four years of layoffs, from 2008-09 through 2011-12. LAUSD sent RIF notices to an average of
13% of teachers per year, with the lowest proportion (eight percent) occurring in 2009-10, the
year that federal government disbursed relief funds through the American Reinvestment and
Recovery Act and the Education Jobs Fund. Importantly given the topic under study in this
paper, Table 1 shows that the majority of teachers who received RIF notices were eventually not
released. In particular, 69% of RIF notices that were distributed were rescinded and 48% of
teachers that were actually laid off returned to the district the following year. Across all four
years of layoffs, 78% of LAUSD teachers that received an initial RIF notice returned to a
teaching position the following year.
3
As noted above, this pattern of providing RIF notices and layoffs to more teachers than is
necessary to meet budget restrictions results from two separate but related aspects of layoff
policy in California. First, California requires school districts to issue RIF notices by March 15
and to notify teachers if their RIF has or has not been rescinded by May 15. At this point,
teachers whose RIFs were not rescinded are let go for the following school year. However,
districts learn more about their budgets and their enrollments over the course of the summer and
into the early fall, and then offer their laid off teachers re-employment when it becomes clear
that they will need to fill positions. This process gives rise to the four main categories of teachers
3
It is not possible to differentiate between teachers that were laid off and offered a job back, but declined to accept
the position, and those that were laid off, offered a job back, and accepted the position. The data show which
teachers received RIF notices and which teachers were laid off, but not which teachers were rehired. If teachers
appear in the dataset as a classroom teacher in the year following a layoff, I assume they were offered a teaching job
and that it was accepted. If teachers do not return to the dataset in the year following a layoff, I assume they were
not offered a position. It is possible, however, that some teachers who were laid off and do not show up in the data
in the following year were offered reemployment and declined. However, this limitation has no bearing on the
current study, as I am interested in what happens to teachers who return to the district after a RIF or a layoff.
14
discussed above and shown in Table 1: (a) teachers who do not receive a RIF notice, and thus
face no direct threat of layoff (No RIF); (b) teachers who receive a RIF notice but then receive
notice that their RIF has been rescinded, thus facing a direct threat of layoff but no actual layoff
(RIF-rescinded); (c) teachers who receive a RIF notice that the district does not rescind, but they
return to LAUSD in the following year as either a full time teacher or a substitute
4
(laid off-
return); and (d) teachers who are laid off and do not return to LAUSD in the following year (laid
off-no return).
Second, California state law requires districts facing budget constraints to lay off teachers
in order of reverse seniority within teaching area. The law holds that seniority must dictate
layoffs unless the district can demonstrate “a specific need for personnel to teach a specific
course of study” and that the more junior employee “has special training and experience
necessary to teach that course or course of study” (California Education Code Sections 44930-
44988). In other words, district administrators may to some extent take into account specific
programmatic needs beyond basic teaching credentials, but they cannot consider other factors
such as teachers’ effectiveness, evaluations, or rates of absenteeism. I will return to this point in
the discussion of analytic methods, as the LIFO process in use in LAUSD enables me to assume
that teachers are not selected for RIF or layoff due to their effectiveness, and especially not due
to their effectiveness as measured by VAMs. In fact, although teachers own VAMs were made
available to them during part of the timeframe under study, principals were not given access to
this information and could not use it in staffing decisions, much less in RIF and layoff decisions.
4
One of the ways principals filled empty teaching positions was to hire short-term substitutes. If these teaching
positions were not filled after approximately a month into the school year, these teachers were classified as long-
term substitutes. I exclude these long-term substitutes in the main analysis, but note that results are consistent if
these teachers are included.
15
These requirements are reflected in the distribution of RIFs and layoffs shown in Table 2.
As expected, the mean experience level of teachers who were laid off was substantially below
the mean experience of teachers who received a RIF but had it rescinded, which was in turn less
than the experience level of teachers who were unaffected by RIFs altogether. Similarly,
elementary teachers were more likely to receive RIFs as compared to middle and high school
teachers. Those with special education and math and science credentials were less likely to
receive RIF or layoff notices.
5
Table 2 also demonstrates that the distribution of RIFs was not
related to value-added measures of effectiveness, after controlling for teacher experience. The
first value-added measure, one-year fixed effects, (used in the regression analyses) is based on
student achievement gains attributable to teachers, estimated separately in each year, without
controls for teacher experience. Consistent with the literature (e.g., Harris & Sass, 2011), I find
that this measure of effectiveness is highly correlated with experience, especially in the early
years of teaching. The second and third value-added measures pool multiple years of data and
use Empirical Bayes shrinkage. The third estimates shown in Table 2 are adjusted for experience.
Clearly, when we compare teacher effectiveness using pooled, shrunken estimates that are
adjusted for experience, we see that there are no significant differences between value-added
measures of effectiveness across layoff conditions. As I describe in further detail below, I use the
non-shrunken one-year fixed effects VAM estimates in all of my analyses; however, I include
experience as a predictor in all models and weight observations by the standard error of the
VAM estimate.
Table 3 makes another important point, and one that is often over-shadowed in the
discussions of RIFs and layoffs, but which took center-stage in the recent Vergara vs. California
5
Although not shown here, my sample of teachers with value-added measures includes a greater proportion of
elementary teachers compared to the overall composition of teachers, reflecting the distribution of teachers for
whom VAM scores can be generated.
16
lawsuit. RIFs and layoffs disproportionately adversely impact students of color in LAUSD. For
instance, Black students were 36 percent more likely to have their teacher receive a layoff notice
and, prior to a State Supreme Court decision protecting highly impacted schools, Latina/o
students were 23 percent more likely to have their teacher laid off.
6
Data and Analytic Approach
As is made clear above, the context in LAUSD affords us a rich case in which to explore
the impacts of the layoff process on teacher effectiveness. Our analyses rely on a panel of teacher
data that includes the years in which RIFs and layoffs were executed in LAUSD. We estimate a
teacher fixed-effects regression model to examine how individual teachers’ effectiveness
changes after the receipt of a RIF notice that is rescinded or a layoff that results in the eventual
re-hiring of the teacher. We measure teacher effectiveness through value-added measures of
teachers’ contributions to their students’ achievement on standardized tests. We elaborate on our
data, identification strategies and methods in the remainder of this section.
Data
I draw on a seven-year panel of administrative data from school years 2006-07 to 2012-
13 that includes information about students, teachers, and schools in LAUSD. These data consist
of anonymized employee demographic information including race/ethnicity, gender, years of
experience, highest-degree earned, school and classroom placement, layoff status, job title,
contract status, salaries, fringe benefits (including pension contributions), and all credentials and
certificates held by each employee. All employee data are linked to anonymized student data
including California Standardized Test (CST) scores in math and English Language Arts,
6
These statistics are not directly inferable from Table 2 as I separate the laid off category by teachers that do and do
not return. Combing the layoff category, I find that 6.3 percent of all Black students have their teachers laid off,
while 4.6 percent of White students’ teachers are laid off. Dividing these numbers give an odds ratio of 1.36. The
disparate impacts of the RIF/layoff process on student subgroups were ameliorated following the decision in Reed v.
California et al, which protected 45 schools in 2010-11 and 2011-12 that would have been most heavily impacted.
17
race/ethnicity, gender, grade level, school and classroom placement, free lunch status, disability
(if any), English language proficiency, home language, course enrollment, teacher assignment,
individual grades for each class and overall grade point average, attendance, suspensions, date of
birth, zip code, and parental education.
I supplement these LAUSD administrative data with public data at the school level from
the California Department of Education, which include each schools’ Academic Performance
Index score (API, a composite score that combines various student performance measures into a
single weighted measure), total student enrollment, number of students tested, and the percent of
students who are classified in the following categories: special education; enrolled in free or
reduced price lunch programs; limited English proficient; reclassified English proficient; African
American; Asian; Filipino; Hawaiian / Pacific Islander; Latino/a; and White. I limit the dataset to
K-12 district schools and dependent charter schools that operate within the district’s collectively
bargained employee contract (and are thus subject to LAUSD RIF and layoff processes). I
exclude non-traditional schools including community day schools, alternative schools, early
education centers, and special education centers because the data are less consistent for non-
traditional schools and it is difficult to derive VAM estimates for many of the teachers in these
schools.
Overall, these data include 3,364,739 student-year observations and 159,229 teacher-year
observations. For school year 2012-13, these data include 528,431 unique students and 23,253
unique teachers in 692 schools.
7
My primary outcome of interest is teacher performance in the
classroom, which I estimate through the generation of value-added models (VAMs) of teachers’
7
I consider teachers to be those personnel who are labeled as “teachers” in the district datasets and who are recorded
teaching at least one class in the school year.
18
contribution to students’ ELA and math achievement scores.
8
In my main analyses, I use school
years 2007-08 to 2012-13, and also include 2006-07 data for some specification checks.
9
The
subset of teachers for which VAMs can be estimated include 31,160 teacher-year observations
for school years 2007-08 to 2012-13.
Analytic Approach
My primary research question asks how teachers’ job performance changes after
experiencing a layoff or threat of layoff. I begin the analysis by fitting teacher fixed effects
models that predict teachers i’s value-added measure in school s in year t (denoted VA
ist
), based
on current and prior year RIF and layoff experience:
(1)
10
The inclusion of teacher fixed effects, τ
is
, in equation (1) allows me to examine changes in
teacher effectiveness within teachers over time. Thus estimates of a teacher’s job performance
are based on that same teacher’s performance in a typical year. My two variables of interest are
RIFre
ist-1
and Layoff
ist-1
, which indicate whether a teacher received a RIF notice that was later
8
Although there is no universally accepted method for estimating teacher value-added effects (e.g., McCaffrey et
al., 2009, Ishii & Rivkin, 2009; Kane & Staiger, 2008; Rothstein, 2009; Todd & Wolpin, 2003), there are several
approaches that represent those commonly utilized in the value-added literature. I use a linear term for all VAM
measures, which identifies an individual teachers’ relative position on the effectiveness distribution. I use the
average estimate between math and ELA for teachers with VAM measures in both subjects. I explore a large number
of alternate specifications, but each measure is based on a value-added model that regresses student achievement
against prior student achievement, a vector of student and family background characteristics, and teacher fixed
effects. My method of generating VAMs is further described in the Appendix.
9
The district only has teacher demographic data available beginning in school year 2007-08. I am able to impute
experience, education, and credential information one year back for the majority of teachers; however, I exclude
these data in my main analysis and only use 2006-07 data in specification checks.
10
For simplicity, I show only the equation used to estimate Model 1, while noting that I run a number of additional
specifications. In particular, I test whether results change when I add controls for (and interactions with) teacher
mobility. I also run models that interact year t-2 and t-1 RIFs and layoffs, whether the teacher returns as a long-term
substitute, and for reasons I describe below, the number of students from which the teacher’s VAM is estimated. I
find that these interactions are generally insignificant and do not substantially alter the coefficients for current and
past year RIFs and layoffs. I also test a series of additional models that include indicators for whether the teacher
received a RIF or layoff notice in year t-2, whether the teacher skipped year t-2, and interactions between t-1 and t-2
layoff conditions. This enables me to assess the differential effects of multiple years of RIFs or layoffs on teacher
effectiveness; however, in all cases, t-2 effects and their interactions are not significant. Results are available from
the author upon request.
VA
ist
= b
0
+b
1
RIFre
ist-1
+ b
2
Layoff
ist-1
+ b
3
X
ist
+ b
4
S
st
+t
is
+m
is
+e
ist
19
rescinded, or received both a RIF notice and a then final layoff notice, respectively, in the prior
year of teaching.
11
The coefficients β
1
and β
2
address my primary research question of how
exposure to threat of layoff or actual layoff is associated with teaching performance in the
following year of teaching.
The vectors X
ist
and S
st
include control variables for time-variant teacher and school
characteristics. At the teacher level, I control for experience and experience squared, whether the
teacher acquired any endorsements such as special education (SPED), science or math (STEM),
or other non-elementary endorsements, and whether the teacher acquired a master’s degree. My
school-level controls include the log of school enrollment, the percent of students that identify as
an underrepresented minority, and the school type (elementary, middle, high school, or span
school).
12
Finally, I also include year fixed effects to remove any changes in teacher
effectiveness that are idiosyncratic to a particular year.
13
In all models, I weight observations are
by the standard error of the value-added estimate and cluster standard errors at the teacher level.
I conduct a number of tests to confirm the causal interpretation of my findings. First, I
test whether teachers that are laid off in year t-1 have lower effectiveness because they switch
schools or grade levels, or because they switch to a long-term substitute position following a
layoff. These models do not change the general results shown in Model 1 of Table 4. Second, I
test for reversion to the mean (for teachers returning to the district following a layoff) by running
models that control for the number of students from which value-added measures are based
(shown in Table 5), as well as the standard error of the value-added measures. I also interact each
11
The reference category for the current year RIF and layoff is teachers that did not receive a RIF notice in year t.
For lagged RIF and layoff variables, the reference category is teachers that did not receive a RIF notice in year t-1.
In those models, I include variables indicating that year t is the teacher’s first year in the district and a variable
indicating that a teacher skipped year t-1.
12
While most teachers remain at the same school type over their window of observation, some switch school types
and these dummies remove any bias associated with these school level switches.
13
Note that my preferred value-added measures are one-year teacher fixed effects and reported in student-level
standard deviations.
20
of these covariates with the variables of interest, RIF and layoff experiences in year t-1. I find
that the number of students from which the VAM is based (usually between 25 and 30 students
for elementary teachers and between 75 and 200 students for middle school teachers), is
significant, but that the magnitude is zero up to the thousandth decimal. None of these additional
covariates substantially alter the significant negative results I find for layoff in t-1.
I also conduct a placebo test in which I estimate the effect of a pseudo RIF or layoff in
the prior year, for teachers that were likely to be assigned to these conditions before the district
began implementing layoffs. To do this, I take the following steps: first, I predict the likelihood
that a teacher receives an initial RIF notice during the years in which layoffs actually took place
(2008-09 to 2011-12). For teachers that did receive an initial RIF notice, I also predict their
likelihood of receiving a layoff notice. I then make an out of sample prediction of each teacher’s
likelihood of receiving an initial RIF and final layoff notice, based on their observable
characteristics in the years before layoffs (2006-07 and 2007-08). Next, I generate a random
number and assign teachers to either the No RIF or the RIF notice condition, based on whether
their random number is above their predicted likelihood of receiving a RIF notice. For those
teachers that are predicted to have received a RIF notice, I assign them to the layoff condition if
their likelihood of receiving a layoff is greater than their random number. I find that during the
years in which layoffs took place, the same proportion of teacher are assigned the placebo RIF
and layoff as were assigned the real values (approximately 13% of teachers receive real or
placebo RIF notices and approximately 4% are laid off). Moreover, between 55% and 60% of the
teachers that received a real RIF or layoff also received a placebo RIF or layoff. In short, this
procedure appears to accurately predict the types of teachers that were likely to receive RIF and
layoff notices in the years prior to layoffs taking place. In the final step of this procedure, I
21
estimate the effect of receiving a pseudo RIF or layoff notice in year t and t-1, for the two years
prior to layoffs actually taking place (2006-07 and 2007-08). I find that the placebo RIF and
layoff variables show virtually no relationship with a teacher’s value-added. Results of these
models are shown in Table 5.
My preferred models provide an unbiased estimate of the impact of RIFs or layoffs if the
selection of teachers into these conditions is unrelated to their measures of effectiveness, after
controlling for observable teacher and school characteristics. I argue that the models provide
unbiased estimates because the distribution of RIF notices and layoffs, as well as the selection of
which teachers to rehire, are highly regulated by state education code. In separate models
predicting RIFs and layoffs, I find that the likelihood of treatment is related only to experience
and credential area, both of which I control for in all models. As described above, the California
Education Code provides clear guidelines on how layoffs are to be determined and the order in
which laid off teachers are offered their jobs back. Administrators have limited flexibility in
deciding which teachers are laid off, and what flexibility exists allows administrators to consider
only credential and teaching position. In addition, principals had no way of ascertaining their
teachers’ VAMs. LAUSD provided teachers with their VAMs (estimated by the Value-Added
Research Center at the University of Wisconsin) for a subset of the years in which RIFs and
layoffs were implemented, but explicit regulations prevented the district from providing
principals with their teachers’ VAMs. The data confirm that LAUSD complied with the
California Education Code in implementing seniority-based layoffs. A causal interpretation is
further supported by the fact that my results are consistent with or without the use of teacher
fixed effects. Finally, if administrators deviated from state-mandated layoff policies, for
example, by offering jobs back only to teachers that were expected to have better than average
22
performance in the following year, such discrepancies would most likely bias estimates upward.
Therefore, any negative relationship I identify between past layoffs and current job performance
will be underestimated if teachers were offered their job back based on expected future
effectiveness.
I note one potential limitation of my identification strategy: I cannot control for teachers’
own selection back into the LAUSD workforce once they have been laid off. Once a teacher is
offered her job back, she has discretion in whether to return to the district. My estimates of the
impact of layoff on effectiveness may be biased if a teacher’s choice to return to the district is
related to her future effectiveness. However, even if selection is driving the fact that teachers
who are laid off and then return to LAUSD are less effective in the following year (relative to
their typical year), this adverse selection is still relevant from a policy perspective. With these
caveats in mind, I present findings in the next section.
Findings
Table 4 shows the results of my preferred model (Model 1) as well as models that include
controls for teacher mobility (Models 2 and 3). I include time-varying teacher and school
controls, but only report the variables of interest as well as the coefficients for experience (results
of the full models showing all covariates are available from the author upon request). The
coefficients on my control variables are consistent with the literature, (e.g.., Feng & Sass, 2011;
Goldhaber, Gross, & Player, 2011; Hanushek, Kain & Rivkin, 2004; Harris & Sass, 2011; Ost,
2014). For example, experience is positively related to measures of effectiveness, especially in
the early years of teaching. My results show that the returns to experience continue at least until
the 10th year of teaching. I find that teachers who acquire graduate degrees (and in models
without fixed effects, those that have graduate degrees) are only slightly more effective than
23
those with bachelors degree and these results are generally not significant. Finally, teachers that
switch schools, switch grade levels, skip a school year, or are new to the district, are generally
less effective than those returning to the same classroom placement from the prior year.
The first model shown in Table 4 (henceforth the baseline model) predicts a teacher’s
measure of effectiveness, based on their prior year RIF or layoff status. This model also controls
for current year RIF or layoff. In Models 2 and 3, I add controls for whether the teacher in a new
school or grade level, respectively, in year t. The results are consistent across all three models:
compared to teachers that were not RIFed the prior year, those that are laid off, but rehired have
lower classroom effectiveness, relative to their typical year. The magnitudes on these coefficients
are both statistically and educationally significant. Given the overall standard deviation of value-
added of 0.276 for years 2007-08 to 2012-13, the coefficient of -0.061 shown in the baseline
model implies that teachers laid off and rehired experienced a decline in effectiveness of 22.1%
of a student-level standard deviation, an amount equivalent to approximately two months of lost
learning time (Lee & Finn, 2010). Teachers threatened with a layoff in the prior year, i.e., those
that received a RIF notice in March of the prior year, but had it rescinded before mid-May of that
year, did not experience changes in their effectiveness compared to those that were not RIFed.
In Table 5, I present a number of additional specifications to test the robustness of these
results. I begin by showing the baseline model from Table 4 for comparison, and then in Model
2, I run an identical specification, but remove the teacher fixed effects. My results are similar in
direction and statistical significance, although the magnitude on layoff in t-1 declines by about a
third. In Models 3 and 4, I run the same models with and without teacher fixed effects,
respectively, but do not control for current year RIFs and layoffs. Again, my result for layoff in t-
1 is significant and the non-fixed effects model shows a similar drop in magnitude, this time
24
declining by about 23%. One potential bias with these results is regression to the mean: it’s
possible that teachers with particularly high value-added are, for whatever reason, more likely to
be laid off, but then return to the district and post lower effectiveness measures the following
year. To that end, Models 5 and 6 control for the number of students from which the teacher’s
VAM is based.
14
Again these models are consistent with the main results found in the baseline
model. I also run models that control for whether the teacher returns to the district in a reduced
FTE position or as a long-term substitute. For all of the covariates just mentioned (indicators for
teacher mobility, current year RIF and layoff, and number of students), I also interact each with
the lagged layoff variables and the results remain consistent (not shown, but available from the
author upon request). Finally, results are also consistent when I substitute a continuous
experience variable and its square for the categorical experience variables.
The last four models shown in Table 5 (Models 7 through 10) are placebo tests based on
the years prior to layoffs (2006-07 and 2007-08). In each of these models, the lagged layoff
coefficient is not significantly different from zero. In models that remove the teacher fixed effect
(Models 8 and 10), we do see a positive and significant correlation between pseudo-RIF-
rescinded in year t-1and current year VAM. However, because this model is only run on two
years of data, and because teacher must be employed in the district the prior year in order to have
been RIFed in the prior year, first year teachers in this model are far more likely to be new to the
district, rather than pseudo-RIFed in t-1 (in the main models, many teachers are new to the
district, but enter with five or more years of teaching). Because teachers make their largest gains
in VAM after their first year on the job, the positive coefficient shown on pseudo-lagged-RIF-
14
As described in greater detail in Kane and Staiger (2002), measures of school or teacher value-added show greater
variability when they are based on smaller sample sizes. This same trend is true in my data. Thus one might expect
to see more reversion to the mean for teachers with VAMs based on smaller number of students. For this reason,
Models 5 and 6 in Table 5 control for the numbers of students from which the VAM is based.
25
rescinded in Models 8 and 10 likely reflects positive gains from the first year of teaching. In
sum, the overall null results of the placebo tests suggest that the findings shown in the baseline
model reflect an underlying causal relationship.
Discussion and Policy Implications
To my knowledge, this paper is the first to assess the impact of the layoff process on
teachers’ own effectiveness. Previous literature has discussed the implications of teacher layoffs
executed through the LIFO process on the overall effectiveness of the teacher workforce, but
none to date has examined how the receipt of a RIF or a layoff followed by a return to the
workforce impacts teachers’ contributions to their students’ test scores. I find that the layoff
process does more than simply remove effective teachers from schools and increase class sizes.
Rather, the general lack of job security brought on by the layoff process negatively affects
teachers’ productivity. In particular, the experience of receiving a layoff notice, but ultimately
being hired back to the district prior the beginning of the school year has a deleterious impact on
teacher effectiveness in the following school year.
These results should not be surprising. For all the reasons I laid out based on the extant
literature, as well as mere common sense, one should expect to see diminishing productivity
from teachers who lose their jobs and remain unemployed for a period of time before being
rehired to the school district. Perhaps what is more surprising is policymakers’ failure to forecast
or recognize this impact.
Even more concerning, it is likely that students of color bear the brunt of any decreases in
teacher effectiveness that result from the layoff process. As I show in Table 2, Black students are
more likely to have teachers who are RIFed and rescinded or laid off and re-hired. If these
teachers are less effective, then there are considerable equity implications attributable to the
26
layoff process that go beyond the churn and instability caused by layoffs in these schools. Simply
put, these traditionally disadvantaged students will face less effective teachers because they are
in schools that are hardest hit by funding cuts.
Layoff procedures in LAUSD are regulated primarily by state Education Code. Across
the nation, states use a variety of laws to regulate teacher layoffs. According to a scan of state
policies conducted for this study, as of the 2012-13 school year, 22 states require that districts
prioritize seniority in the determination of layoffs. 17 states explicitly require that local districts
determine layoff procedures, or implicitly require them to do so by leaving layoff policies out of
the Education Code (see also: Dowell, et al., 2011). Finally, 11 states require that districts use
criteria other than seniority and six of those states reformed their layoff policies as recently as the
2011-12 school year (Illinois, Louisiana, Michigan, North Carolina, Texas, and Utah).
Differences in states policies regulating teacher layoffs reflect differing preferences and
interpretations around workers' rights and educational policy (Winkler, Scull, & Zeehanelaar,
2012). Of the six states that recently changed their policies regulating layoffs, five are Right-to-
Work states in which unions generally have less influence over state policymaking. Two other
states that generally have stronger teachers unions, California and New York, have recently
undergone litigation over layoff policies (Vergara v. California; Davids v. New York).
There are multiple ways that policy might be amended to mitigate the harmful and
disparate effects of RIFs and layoffs on teacher effectiveness. The simplest way would be to
maintain a source of additional revenue that can be used in times of emergency. Alternatively,
the state could remove or delay the requirement that districts notify teachers of an impending
layoff. By enabling districts to develop more accurate budget projections, and as a result more
accurate staffing requirements, fewer teachers will be impacted by the layoff process. At the
27
same time, it is not clear from the data presented in this study if it is beneficial to teachers to
receive this early notification. The underlying challenge for districts is that they do not receive
their final state budget until the June or July. Thus states could be required to develop their
budget projections by a specified time, enabling districts to have better information before they
make staffing decisions (of course, states are supposed to release final budgets early in the year,
but California, like other states, often misses this deadline with little or no resulting
consequence).
Policymakers can also generate new policies to improve the inequitable outcomes of
layoffs on traditionally disadvantaged students. One such policy was implemented in LAUSD as
a result of the lawsuit Reed vs. California. Plaintiffs in Reed sued LAUSD because particular
classifications of students (low-income and underrepresented minority) were adversely affected
by layoffs. As a result, the settlement resulting from Reed required that LAUSD protect teachers
in a set of schools from layoffs. In this way, students in the most impacted schools were
protected from the churn, instability, and, according to the results presented here, diminished
effectiveness associated with the layoff process. Finally, policymakers might soften requirements
to RIF and lay off teachers in order of reverse seniority and to re-hire in order of seniority. This
will help assuage the negative impact of the layoff process for two reasons. First, LIFO processes
by necessity require laying off more teachers than would be necessary under other selection
regimes (junior teachers are less expensive than more senior teachers). If districts used
alternative selection mechanisms for layoffs, fewer teachers, and as a result fewer students,
would be impacted. Second, and perhaps more importantly, because schools with greater
proportions of low-income and minority students attend schools with more junior teachers, the
LIFO process causes low-income and minority students to be disproportionately impacted by the
28
layoff process. By diminishing the reliance on seniority in layoff processes, policymakers might
also reduce the negative impact of layoffs and RIFs on low-income and minority students. That
said, many teachers consider LIFO policies as an objective and fair mechanism for determining
layoffs (Barkan, 2011). Changing such policies may have unintended consequences on teachers’
working conditions.
The results from this paper also provide lessons for policymakers and researchers as we
consider policies that rely on accurate measures of teacher effectiveness. For instance, popular
teacher evaluation and tenure reforms require staffing decisions to be made in part based on
measures of teacher effectiveness. However, my results suggest that teachers impacted by the
layoff process may be less effective simply as a result of receiving a layoff notice. Policymakers
and administrators should account for these likely dips in effectiveness when considering how to
evaluate and assess teacher performance in high-stakes settings. Similarly, researchers evaluating
the impact of reforms on teacher effectiveness should take care to include controls for teachers’
prior RIF and layoff status. If layoff threat goes unaccounted for in models assessing the
effectiveness of interventions on teacher performance, researchers run the risk of biasing their
impact estimates.
In all, this paper suggests that there are hidden costs of the layoff process that must be
considered in the larger policy conversation around RIFs, layoffs, and teacher effectiveness. It is
my hope that researchers, policymakers and administrators can learn from the Great Recession
and improve upon policies enacted during this time in ways that can lessen negative impacts of
the next financial crisis.
29
Tables
Table 1. Proportion of teachers exposed to various layoff conditions, by year
2008-09 2009-10 2010-11 2011-12 Total
No-RIF
24,212 24,577 22,070 21,259 92,118
RIF-rescinded
3,064 1,826 2,492 2,315 9,697
Layoff-rehire 456 212 770 699 2,137
Layoff-no rehire 1,356 143 500 309 2,308
Total 29,088 26,758 25,832 24,582 106,260
% of teachers RIFed 16.8% 8.2% 14.6% 13.5% 13.3%
% laid off of those RIFed 37.2% 16.3% 33.8% 30.3% 31.4%
% let go of those RIFed 27.8% 6.6% 13.3% 9.3% 16.3%
% let go of those laid off 74.8% 40.3% 39.4% 30.7% 51.9%
30
Table 2. Descriptive statistics for teachers by layoff status, 2008-09 to 2011-12
%
Overall
No RIF
RIF
RIF-
rescinded
Laid off-
return
Laid off-
no return
All Teachers 106,260
92,118 9,697 2,137 2,308
86.7% 9.1% 2.0% 2.5%
Experience / Education
Novice teachers (1st - 3rd year) 7.0% 61.0% 15.9% 6.9% 16.2%
Mid - career teachers (4th - 8th year) 22.1% 68.3% 23.6% 4.9% 3.2%
Veteran teachers (9th year or above) 70.9% 95.0% 3.9% 0.6% 0.5%
Mean years of experience 10.1 10.9 6.0 4.8 3.4
Master’s degree or higher 36.5% 87.2% 9.3% 1.8% 1.7%
Endorsement Area
Special Ed 14.7% 96.9% 1.7% 0.8% 0.7%
Health/PE 5.5% 92.3% 2.9% 1.1% 3.7%
Science 5.9% 92.3% 6.5% 0.5% 0.7%
Foreign Languages 3.1% 91.5% 4.0% 2.1% 2.4%
Math 7.3% 89.8% 9.2% 0.5% 0.6%
Agriculture/Tech/Other elective 4.4% 88.7% 5.9% 3.1% 2.3%
Social Studies 9.1% 87.6% 7.4% 2.7% 2.2%
English/LA 11.4% 85.4% 9.3% 2.3% 2.9%
Elementary Ed 42.7% 82.8% 12.7% 2.4% 2.2%
Arts 6.9% 81.8% 10.3% 4.5% 3.4%
Value-added measures
1-year fixed effects estimates
-0.02 -0.02 0.01 -0.01 -0.08
(0.274) (0.276) (0.274) (0.286) (0.228)
Pooled estimates with EB shrinkage
0.01 0.01 0.02 0.00 -0.06
(0.205) (0.207) (0.201) (0.195) (0.174)
Experience adjusted pooled estimates with EB
shrinkage
0.00 -0.01 0.04 0.03 0.01
(0.205) (0.207) (0.201) (0.198) (0.178)
Mobility
Stay at school 84.8% 88.2% 79.1% 55.4% 0.0%
Leave school 15.2% 11.8% 20.9% 44.6% 100.0%
Switch schools in district 7.2% 6.0% 12.5% 44.6% 0.0%
Leave district 8.0% 5.8% 8.4% 0.0% 100.0%
Note: the % overall column shows the overall percent of teachers with that experience / education level,
endorsement area, or mobility outcome. For teacher characteristics and endorsement areas by layoff status, rows
sum to 100% within threat types (No RIF, RIF-rescinded, laid off-return, and layoff-no return), whereas in the
mobility panel, columns sum to 100% for stay and leave school. For teachers that leave their school, the final two
rows of the table show whether they switch schools within the district or leave the district and or leave switch to a
non-teaching position.
31
Table 3. Descriptive statistics for students by layoff status, 2008-09 to 2011-12
% Overall No RIF
RIF
RIF-
rescinded
Laid off-
return
Laid off-
no return
English Language Fluency
Fluent, non-native English 10.9% 11.0% 10.6% 9.9% 11.2%
Limited English prof. 37.7% 37.9% 35.8% 38.4% 37.9%
Reclassified English prof. 15.2% 14.9% 17.5% 16.6% 14.0%
Native English speaker 36.2% 36.3% 36.1% 35.1% 37.0%
Race / Ethnicity
Asian 5.0% 5.0% 5.2% 5.2% 3.9%
Black 8.9% 8.4% 11.2% 10.8% 13.4%
Filipino 1.0% 1.1% 0.9% 1.0% 1.7%
Latina/o 75.0% 75.5% 72.8% 73.2% 71.8%
Native American 0.3% 0.3% 0.3% 0.3% 0.4%
Pacific Islander 0.3% 0.3% 0.4% 0.3% 0.2%
White 9.3% 9.4% 9.3% 9.2% 8.6%
Family income
FRL 78.5% 78.5% 78.1% 78.5% 67.5%
non-FRL 21.5% 21.5% 21.9% 21.5% 32.5%
Note: percentages sum to 100% vertically in each panel. For example, of all students in LAUSD, 10.9% are
classified as fluent non-native English speakers, whereas 11.0% of students whose teacher was not RIFed are
classified as fluent non-native English speakers. FRL indicates students that are eligible for the federal Free or
Reduced Price Meal program.
32
Table 4. Regression coefficients predicting teacher effectiveness based on prior year layoff
experiences, 2007-08 to 2012-13
(1) (2) (3)
Year t-1 layoff threat
RIF-rescinded in t-1
-0.009 -0.008 -0.009
(0.006) (0.006) (0.006)
Laid off in t-1
-0.061*** -0.050*** -0.060***
(0.015) (0.015) (0.015)
Skipped year t-1
-0.059+ -0.037 -0.058+
(0.031) (0.032) (0.031)
Year t is first year in district
-0.031+ -0.035* -0.036*
(0.016) (0.016) (0.016)
Prior year mobility
New to school (but not district)
-0.034***
(0.006)
New to grade
-0.015***
(0.004)
Teacher characteristics
1st year (ref is >13th year)
-0.145*** -0.144*** -0.147***
(0.030) (0.030) (0.030)
2nd year
-0.099*** -0.099*** -0.100***
(0.023) (0.023) (0.023)
3rd year teacher
-0.095*** -0.096*** -0.096***
(0.021) (0.020) (0.020)
4th year teacher
-0.053** -0.054** -0.053**
(0.018) (0.018) (0.018)
5th year teacher
-0.047** -0.047** -0.046**
(0.017) (0.016) (0.016)
6th or 7th year teacher
-0.036** -0.036** -0.036**
(0.014) (0.014) (0.014)
8th - 10th year teacher
-0.020+ -0.020+ -0.020+
(0.011) (0.011) (0.011)
11th - 13th year teacher
-0.011 -0.010 -0.011
(0.008) (0.008) (0.008)
Master's or higher degree
0.008 0.009 0.008
(0.012) (0.012) (0.012)
Constant
0.388*** 0.438*** 0.397***
(0.074) (0.074) (0.074)
R-squared 0.727 0.728 0.728
Note: n=31,160 in all models. All models shown here control for time-varying teacher characteristics including
current year layoff status and teaching credentials as well as school-level covariates including the log of enrollment,
percent of students that identify as an underrepresented minority, and school level (elementary, middle, or high
school). Models also include year fixed effects. Robust standard errors shown in parentheses (clustered at the
teacher level).
33
Table 5. Additional specification checks of models predicting teacher effectiveness based on prior year layoff experiences, 2007-08 to
2012-13, and placebo tests, 2006-07 and 2007-08
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
Year t layoff threat
RIF-rescinded in t
0.003 0.013*
0.004 0.006
-0.005 0.012+
(0.006) (0.005)
(0.006) (0.006)
(0.016) 0.007
Laid off in t
0.000 -0.012
0.001 0.005
0.002 -0.009
(0.010) (0.008)
(0.010) (0.010)
(0.023) 0.009
Year t-1 layoff threat
RIF-rescinded in t-1
-0.009 0.009+ -0.009 0.014* -0.005 -0.004 -0.005 0.020* -0.010 0.020*
(0.006) (0.005) (0.006) (0.006) (0.006) (0.006) (0.016) 0.01 (0.021) 0.009
Laid off in t-1
-0.061*** -0.041*** -0.062*** -0.047*** -0.050*** -0.045** 0.013 -0.004 0.015 -0.003
(0.015) (0.012) (0.015) (0.013) (0.015) (0.015) (0.021) 0.011 (0.029) 0.011
Skipped year t-1
-0.059+ -0.125*** -0.059+ -0.129*** -0.049 -0.046
(0.031) (0.020) (0.031) (0.021) (0.031) (0.031)
Year t is first year in
district
-0.031+ -0.065*** -0.031* -0.065*** -0.030+ -0.048***
-0.050***
-0.051***
(0.016) (0.011) (0.016) (0.011) (0.016) (0.013)
0.011
0.011
N 31,160 31,160 31,160 31,160 31,160 31,160 10,782 10782 10,782 10782
R-squared 0.727 0.123 0.727 0.123 0.728 0.728 0.898 0.364 0.898 0.364
Teacher fixed effects X
X
X
X
X
Note: Model 1 is the baseline model shown in Table 4 and Model 2 is an identical model that removes teacher fixed effects. Models 3 and 4 remove year t layoff
controls, with and without teacher fixed effects, respectively. Models 5 and 6 control for the number of students on which each teacher’s VAM is based, again
with and without teacher fixed effects. Models 7 through 10 are placebo tests showing that the lagged layoff coefficient is insignificant, with or without fixed
effects. Because the placebo tests are run only on two years of data, 2006-07 and 2007-08, no teachers could have skipped the prior year (and that variable drops
out of the model). For a similar reason, in placebo models with fixed effects, the coefficient for new to the district drops out because teachers that were new in
2007-08 were, by definition not present in 2006-07 and therefore there is no variation from which to estimate this coefficient. The reference category for year t
layoff threat is not RIFed in year t and the reference category for year t-1 layoff threat is not RIFed in year t-1.
34
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41
Appendix
Estimating Value-Added Measures of Effectiveness
For elementary teachers who teach in self-contained classrooms (with the same group of
students all day), I estimate the following model, for each school year from 2006-07 to 2012-13:
A
ijs
=a
1
A
ijs(t-1)
+a
2
A
ijs(t-1)
alt
+a
3
X
ijs
+t
js
+e
ijs
,
where A
ijs
is a measure of student i’s achievement on either the math or English subjects of the
California Standardized Test (CST), standardized within year and test, A
is(t-1)
and the alternate
(“alt”) represent lagged test scores in the current subject (math or English) and in the alternate
subject (math if the model is estimating English achievement, English if the model is estimating
math achievement). X
it
represents vectors of student characteristics that include family income,
as measured by eligibility for free and reduced price lunch, English language proficiency,
whether English is spoken in the home, whether the student is enrolled in any additional courses
in math or English (depending on which test score is being predicted), whether the student is in
his or her first year in the district, and dummy indicators for students’ grade level. Finally,
js
represents a teacher fixed effect and the error term, ε
ijs
, is assumed independently identically
distributed with respect to the other variables in the model. I also test several variations of this
basic model including: 1) pooling all years of data and estimating teacher effectiveness over
multiple years and including year fixed effects (Koedel & Betts, 2007; McCaffrey, Sass,
Lockwood, & Mihaly, 2009); 2) removing the student covariates and adding a twice-lagged
measure of student achievement, as suggested by Rothstein (2010); and 3) adding classroom and
school covariates to control for peer effects (Lubienski & Lubienski, 2013)
Standard testing in California spans from grade two through grade eleven, thus it is
possible to generate VAM for teachers in grades three through eleven (grade two teachers’
42
students do not have pretests and therefore must be excluded). I test whether the estimates of
value-added measures are sensitive to the particular students and teachers included in the model.
In particular, I generate VAMs for just students in grades three and four, four and five, five and
six, six and seven, seven and eight, eight and nine, nine and ten, ten and eleven, and every
combination thereof (three through five, three through six, three through seven, ect., four through
five, four through six, ect.). I do this because there are idiosyncrasies specific to certain grades.
For example, because of regulations in California Education Code, class sizes in grades
Kindergarten through third grade are significantly smaller compared to those in grades four and
higher. Beginning in grade eight, students enrolled in the same course can take three different
math tests, which complicates estimation of VAMs beyond grade seven. In general, all of these
models are highly correlated, usually at 0.98 or higher. However, in all analyses presented in this
paper, I use the most conservative value-added estimate, which limits the sample of teachers to
just those in grades four through seven (omitting grade three teachers due to their smaller class
sizes and teachers in grades eight and above because in these grades, students in the same class
can take different standardized exams). I also tests my preferred baseline model using all of these
grade span VAMs and my results are generally consistent across all of these specifications
(results are available from the author upon request).
For models in which I include middle and high school students, a few adjustments are
necessary because classrooms are not self-contained. In these models, I add dummy variables for
which post-test students took and interact these indicators with students’ pre-test score. This
approach controls for the potential non-random selection of students to particular tests as well as
the measurement error associated with each test (Isenberg & Hock, 2010). Although my
preferred VAM estimates for this study are the raw (non-shrunken) measures, in alternate
43
specifications, I conduct a Bayesian shrinkage procedure (Herrmann, Walsh, Isenberg, & Resch,
2013). The purpose of shrinking teacher effects is to adjust toward the mean those teachers with
extreme scores (either high or low) but imprecise measures, while making only a trivial
adjustment to those with precisely measured extreme scores (Goldhaber & Theobald, 2013). In
my main analysis, I do not use shrunken estimates and instead weight observations in the
regression by the standard error of each teachers’ estimate value-added.
Abstract (if available)
Abstract
Massive funding cuts to public education in the United States, brought on by the Great Recession of 2008, forced school districts around the country to reduce budgets and conduct layoffs. Hundreds of thousands of educators lost their jobs, but many were rehired during the summer months when districts receive final budgets for the following school year. This study is the first to estimate the impact of exposure to layoffs and layoff threat on teacher effectiveness. The author draws on administrative panel data provided by the Los Angeles Unified School District. Measures of teacher job performance are estimated using student achievement gains on standardized exams. Teachers who were laid off, but rehired prior to the beginning of the following school year experienced declines in their measure of effectiveness of approximately 0.061 standard deviations compared to their typical year, or about two months of student learning time. Black and Latina/o students were far more likely than white students to see their teacher laid off. The author recommends that states and districts identify budget and layoff notification timelines that are more in sync and that districts implement layoff policies that ensure they are equitably distributed across students and schools.
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Are there hidden costs associated with teacher layoffs? The impact of job insecurity on teacher effectiveness in the Los Angeles Unified School District
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