The Multidimensional Impact of Teachers on Students

Nathan Petek and Nolan G. Pope*

January 2021

Abstract

For decades, policymakers and researchers have used value-added models that rely solely onstudent test scores to measure teacher quality. However, since teaching ability is multidimen-sional, test-score value-added measures of teacher quality may not fully capture the impact ofteachers on students. In this paper, we use test-score and non-test-score measures of studentachievement and behavior from over a million students in the Los Angeles Unified SchoolDistrict to estimate multiple dimensions of teacher quality. We find that test-score and non-test-score measures of teacher quality are only weakly correlated, and that both measures ofteacher quality affect students’ performance in high school. A teacher-removal policy simula-tion that uses both dimensions of teacher quality improves most long-term student outcomesby over 50 percent compared to a policy that uses test scores alone. Our results also show thatthe long-term effects of teachers in later grades are larger than in earlier grades and that per-formance in core elementary school subjects matters more for long-term outcomes than othersubjects.

*Petek: Federal Trade Commission, 600 Pennsylvania Ave NW, Washington, DC 20580 (npetek@ftc.gov).Pope: The University of Maryland, 3114 Tydings Hall 7343 Preinkert Dr, College Park Maryland 20742(pope@econ.umd.edu). We would like to thank John Eric Humphries, Steven Levitt, Jens Ludwig, Ofer Malamud,Magne Mogstad, Derek Neal, and Eric Nielsen for helpful comments and discussion. The views expressed in thisarticle are those of the authors. They do not necessarily represent those of the Federal Trade Commission or any of itsCommissioners.

1 Introduction

Teacher quality has garnered the attention of policymakers and researchers for many years. Re-

searchers have primarily measured teacher quality using a test-score value-added framework.1 Al-

though the use of test-score value-added has substantially impacted education research, people

have long recognized that good teachers likely affect a wide range of student outcomes. In fact,

early theoretical formulations of value-added used an education production function that modeled

educational output as a “multidimensional factor” (Hanushek 1971). Consequently, measures of

teacher quality that rely solely on student test scores may not fully capture the impact of teachers

on students.

In this paper, we are interested in whether teachers can noticeably impact measures of student

achievement beyond just test scores, and to what extent the impact on non-test-score measures

is important for the future success of students. We use the value-added framework to construct

separate measures of teacher ability to increase test scores, behavior, and a plausible measure of

noncognitive skills. We use these multiple value-added estimates of teacher ability to explore

the effect of teachers on students’ long-term outcomes and the relative importance of cognitive

and noncognitive skills in the production of human capital. We illustrate the benefits of using

broader measures of teacher ability by investigating the extent to which using multiple measures

of teacher ability increases the efficacy of teacher selection and assignment policies, improves the

measurement of the cumulative return to high quality teaching, and allows the measurement of

teacher quality in untested subjects.

We gather administrative data from the Los Angeles Unified School District (LAUSD) for stu-

dents in grades K-12 from 2003 to 2015. These data link over a million students to teachers, and

track students over time as they progress through the LAUSD system. Our three measures of stu-

dent achievement are constructed from (1) student math and English state test scores, (2) measures

of student behavior, including suspensions, attendance, GPA, and grade retention, and (3) teacher

1An important exception is a paper by Kirabo Jackson (2018) that estimates non-test-score measures of teacherquality that we discuss in more detail below.

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assessments of student effort and 14 learning skills which are plausibly measures of noncognitive

ability. The learning skills include teacher assessments such as whether a student makes good use

of time, exercises self-control, and resolves conflicts appropriately. We measure the long-term ef-

fects of teachers using student performance in high school, including dropping out of high school,

taking the SAT, SAT scores, high school exit exam scores, GPA, teacher assessments of effort and

cooperation, attendance, suspensions, and grade retention.

We first document that elementary school students with better test scores, behavior, and learn-

ing skills perform better in high school. We then estimate teacher value-added measures of three

dimensions of teacher quality – student test scores (using math and English state tests), student

behavior (using GPA, attendance, suspensions, and grade retention), and student learning skills

(using teacher assessments of effort and 14 learning skills). To avoid bias and potential teacher

manipulation when using teacher-reported non-test-score variables, we modify the standard value-

added framework to use student outcomes from the year after the student was in a teacher’s class,

instead of the contemporaneous year. Using these value-added measures, we show that teachers

affect both test-score and non-test-score dimensions of student achievement, but little evidence that

that learning-skills value-added in particular affects high school outcomes.

We find that having a high test-score value-added teacher in elementary school improves stu-

dents’ high school performance. These long-term effects of test-score value-added are not sub-

stantially reduced by adding teachers’ behavior or learning-skills value-added to the model. This

result suggests that the long-term effects of test-score value-added may not be biased by omitting

non-test-score teaching ability.

We also find that behavior value-added is only weakly correlated with test-score value-added,

and has a similarly large effect on students’ long-term outcomes. Therefore, test-score value-added

misses the dimensions of teacher quality captured by behavior value-added that matter for long-

term outcomes. Consequently, test-score value-added underestimates the total effect of teachers

on students. However, we find no evidence of an interaction effect for teachers who are better or

worse on both dimensions of teacher ability.

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The low correlation between the two value-added measures also suggests that using behavior

value-added in conjunction with test-score value-added may substantially enhance the accuracy

with which overall teacher quality is measured. We illustrate how behavior value-added enhances

the measurement of teacher quality using a hypothetical policy simulation that replaces teachers

in the bottom 5 percent of the teacher quality distribution with district average teachers. Relative

to relying on test-score value-added alone, a simple rule that equally weights the test-score and

behavior value-added of a teacher increases the efficacy of the policy by at least 50 percent for

the likelihood of dropping out of high school, taking the SAT, high school GPA, suspensions,

absences, and on-time progression. These gains are obtained with little to no decline in student

test scores, are similar to the gains obtained if an optimal weighting scheme is used, and do not

require administering additional tests or using data beyond what schools typically collect.

Finally, we use test-score and non-test-score measures of ability in two applications. First,

we estimate the effect of test-score and behavior value-added for each grade 3 to 12. We find

that middle school and high school teachers have a larger effect on outcomes measured in 11th or

12th grade than elementary school teachers. This result suggests that teachers in later grades may

play a more important role in benefiting the long-term student outcomes than teachers in earlier

grades under the strong assumption that a one standard deviation change in teacher value-added

induces the same amount of learning in each grade. Assuming constant returns to higher quality

teachers, these results imply large cumulative benefits of teacher value-added. For example, giving

students a standard deviation better test-score value-added teacher each year from grades 3 to 12

increases the likelihood of taking the SAT by 8.1 percentage points, and reduces the likelihood

of dropping out of high school by 0.5 percentage points. Giving students a standard deviation

better behavior value-added teacher over the same period increases the likelihood of taking the

SAT by 8.4 percentage points, and reduces the likelihood of dropping out of high school by 5.9

percentage points. The cumulative effects of better teachers are only somewhat reduced by controls

for tracking.

Second, the focus on test scores has limited the study of teacher effects to a few regularly tested

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subjects (i.e., math and English). We instead use subject-specific GPAs to compute value-added

measures of teacher quality in 10 elementary school subjects. We find that students with higher

value-added teachers in reading and health perform better in high school, whereas having a higher

value-added teacher in speaking has negative effects on high school performance. Hiring teachers

who are relatively better at teaching reading and spending more time on it could potentially benefit

the long-term outcomes of student.

From a policy perspective, there are potentially large benefits from adopting a measure of

teacher quality that includes both test-score and non-test-score dimensions. For example, poli-

cymakers can use non-test-score value-added to measure teacher quality for all teachers, not just

math and English teachers. In addition, since focusing on only one output of the multidimensional

education production function (i.e., test scores) may distort the efficient allocation of teachers’ time

and resources, using a broader measure of teacher quality may help alleviate this distortion. Fi-

nally, using a better measure of overall teacher quality can make school districts’ hiring and tenure

decisions more effective.

Our paper contributes to a literature that estimates the effect of various non-test score value-

added measures on contemporaneous outcomes (Jennings and DiPrete 2010, Ruzek et al. 2014,

Gershenson 2016, Blazar and Kraft 2017, and Kraft 2019), on within-high school outcomes (Jack-

son 2018), and on outcomes of 20-year-olds (Fleche 2017).2 The paper most closely related to ours

is Jackson (2018). Using North Carolina data, he finds that above and beyond test scores, teachers

effect proxies for noncognitive skills (behavior value-added) in 9th grade and subsequently out-

comes in 12th grade such as high school completion, SAT-taking, and intentions to attend college.

He finds that including both test-score and behavioral value-added measures in 9th grade more

than doubles the predictable variability of teacher effects on 12th grade outcomes.

2The non-test-score value-added measures include social and behavioral skills (Jennings and DiPrete 2010); mo-tivation (Ruzek et al. 2014); absences (Gershenson 2016); belief in the ability to do math and happiness in math class(Blazar and Kraft 2017); and grit, growth mindset, effort; answering open-ended questions (Kraft 2019); absences, sus-pensions, grades, and grade progression (Jackson 2018); and internalizing and externalizing behavior (Fleche 2017).Araujo et al. (2016) produce short-term estimates of classroom value-added on measures of executive function. Otherstudies assess multidimensional teacher effects using non-value-added approaches (Mihaly et al. 2013 and Rockoffand Speroni 2010).

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Our paper contributes to the literature in several ways. First, we show the long term effects of

non-test-score value-added extend back to elementary school. We also show there are no interac-

tions in the educational production function between our measures of teacher ability to increase

cognitive and noncognitive skills. In addition, we quantify the potential gains to long-term student

performance from a policy that assesses teachers using both test-score and non-test score value-

added rather than just test-score value-added.

Second, our estimates of the long-term effect of teacher quality by grade (for both test-score

and non-test-score measures) allow for estimates of the benefits of switching teachers between

grades and the cumulative effect of increasing teacher quality across grades that is not possible

in previous work (Chetty, Friedman, and Rockoff (2014b) estimated test-score value-added for

grades 4 through 8 and Jackson (2018) for test-score and non-test-score value-added in grade 9).

The test-score value added literature also has been limited to measuring teacher performance in

tested subjects like English and math (Jackson, Rockoff, and Staiger 2014). Our extension of the

value-added framework to measure subject-specific GPA value-added provides novel estimates of

the effect of teaching quality on long-term outcomes in subjects outside of English and math.

Finally, we contribute to the broader literature of the role of cognitive and noncognitive skills

in the production of human capital and long-term outcomes (e.g., Cunha, Heckman, Schennach

2010; Heckman, Stixrud, and Urzua 2006) by analyzing how teachers with varying levels of ability

to increase students’ cognitive and noncognitive skills affect their students’ long-term outcomes.

More specifically, we contribute to understanding the role that the development of cognitive and

non-cognitive skills play on the long-term effects of educational interventions (Heckman, Pinto,

and Savelyev 2013) using a different source of variation in cognitive and noncognitive skills.

The rest of the paper will proceed as follows. Section 2 provides background information on

value-added scores. Section 3 describes the LAUSD data that we use and, in particular, describes

the variables used to measure test-score, behavior, and learning-skills value-added. Section 4 out-

lines the empirical method for estimating teacher value-added measures and estimating the effect

of teacher value-added on long-term student outcomes. Section 5 presents the descriptive results of

6

the test score, behavior, and learning-skills value-added of teachers, and then reports the results for

how teachers affect students’ concurrent and long-term outcomes. The gains from teacher-removal

policies that use multiple dimensions of teacher quality are also presented. Section 6 presents the

relative value of higher quality teachers over the students’ educational life cycle and in specific

subjects. Section 7 concludes.

2 Background on Test-Score Value-Added

Since the early 1970s, researchers have used test-score valued-added to measure teacher quality

(Hanushek 1971). This research led states and school districts to use test-score value-added in

teacher evaluations as early as the 1990s (Horn and Sanders 1994). Since then, the use of test-score

value-added has expanded, and 27 states require that teacher evaluations include “growth measures

as a significant criterion” (The National Council of Teacher Quality 2015). This increased use

of test-score value-added has largely been due to a lack of other predictors of teacher quality

(Hanushek and Rivkin 2010). Much of the recent work in the value-added literature focuses on the

validity of value-added models (Bacher-Hicks, Kane, Staiger 2014; Chetty, Friedman, and Rockoff

2014a; Kane and Staiger 2008; Kane et al. 2013; Rockoff 2004; Rothstein 2010; Rothstein 2017;

Chetty, Friedman, and Rockoff 2017), gains from using them in personnel decisions (Goldhaber

and Hansen 2010; Gordon, Kane, and Staiger 2006; Hanushek 2011), and theoretical and empirical

studies of their use in pay-for-performance (Fryer 2013; Goodman and Turner 2013; Neal 2011).

In particular, Chetty, Friedman, and Rockoff (2014b), who find that students with higher test-score

value-added teachers earn significantly more by their late 20s, have fewer births as teenagers, and

are more likely to attend college.

3 Los Angeles Student Data

The LAUSD is the second largest school district in the United States, educating over 600,000

students each year. In 2003, the school district was 71.9 percent Hispanic, 12.1 percent black,

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and 9.4 percent white.3 We use a panel of student-level administrative data on all public school

students in the LAUSD. The panel links students to teachers over time and includes the 2002-03

to 2014-15 school years, which we reference by year of graduation (e.g., we refer to the 2002-03

school year as 2003). Our analysis focuses on the over 110,000 3rd to 5th grade students studying

in the LAUSD each year.

These data are unique in the level of detail they provide about each student’s academic perfor-

mance. For grades 2 through 11, math and English California state test (CST) scores are available

for each student. The testing regime is relatively consistent over this period, with the only major

change being an essay section added to the 4th- and 7th-grade English test in 2011. For all grades,

these data contain the number of days a student was suspended, the number of days a student was

absent, and whether a student did not progress on time to the next grade (i.e., held back). Both

elementary and high school students received progress reports with their grades by subject and a

number of additional teacher assessments of student performance.

Elementary school progress reports (grades K-5) are given each trimester by the student’s sole

classroom teacher, and contain achievement grades in 10 subjects (e.g., reading, mathematics, art,

etc.), effort grades for the same 10 subjects, grades for five “work and study habits” (e.g., “makes

good use of time,” “organizes materials,” etc.), and grades for nine “learning and social skills”

(e.g., “resolves conflicts appropriately,” “exercises self-control,” etc.). All grades are on a 4-point

scale, with no fractional points given. We compute an annual GPA for each of the four groups

listed above. Figure 1 shows a template of the progress report.

Starting in the 6th grade, middle school and high school students receive progress reports each

semester from multiple classroom teachers, with three categories of grades for each of their classes:

achievement (i.e., academic performance), “work habits,” which we term effort (i.e., “effort,” “re-

sponsibility,” “attendance,” and “evaluation”), and “cooperation” (i.e., “courtesy,” “conduct,” “im-

provement,” and “class relations”). Achievement is graded on a 4-point scale, and effort and

cooperation are graded on a 3-point scale, with no fractional points given. We compute annual

3Statistics can be found at http://dq.cde.ca.gov/dataquest.

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GPAs for each of these three groups of measures. Appendix Figure A.1 shows additional details

on grading criteria.

Additional data are available for middle and high school students, including whether a student

dropped out of high school (i.e., the student enrolled in the LAUSD in grade 9 and did not graduate

high school in the LAUSD within 5 years), graduated from the LAUSD conditional on enrolling

in the LAUSD in 12th grade, SAT scores, PSAT scores, math and English California High School

Exit Examination (CAHSEE) scores, science CST scores (grades 5, 8, and 10), social science CST

scores (grades 8, 11, and world history), and the number of AP courses taken. All test scores are

normalized to be mean zero and standard deviation one at the grade-year level, except both SAT

and PSAT scores, which we place on a 600-2400 scale (PSAT is normally on a 60-240 scale, and

for some years, the SAT was on a 400-1600 scale). We top code days absent at 180 days per year,

and report log absences as the log of one plus the number of absences.

Described fully in sections 4.1 and 4.2, we compute test-score value-added measures using

math and English CST scores, and behavior value-added using log days absent, achievement GPA,

an indicator for suspensions, and an indicator for being held back. For elementary school teach-

ers, we compute learning-skills value-added using the three additional types of elementary school

GPAs. We reduce the dimensionality of both the inputs to the value-added variables and the value-

added variables themselves by creating equally weighted indices.

Our main outcome variables are measures of high school performance, including an indicator

for dropping out of high school, an indicator for taking the SAT, SAT scores, the three high school

GPA measures averaged from grades 9-12, math and English California high school exit-exam

scores, days suspended in grades 9-12, log absences in grades 9-12, and an indicator for being held

back in grades 9-12. We treat graduation as a supplemental measure because it is conditional on

enrolling in the LAUSD in 12th grade. The unit of observation in the main analyses data set is a

student-academic year, where the outcome is typically a measure of the student’s performance in

high school and the independent variables of interest are the three teacher value-added indices for

a student in a particular academic year. We focus on the pooled effect of teachers in grades 3, 4,

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and 5, and thus the same student often appears in the main analysis three times.

Summary statistics for these data are shown in Table 1. Panel A shows summary statistics for

all students in grades 3-5 from the 2004 to 2010 school years. Panel B shows high school summary

statistics for the students in Panel A who attended high school in the LAUSD. Appendix Figure

A.2 shows retention rates by grade. While the dropout numbers seem high, they are consistent

with LAUSD’s official graduation rate which was 64% in 2005-2006 to 62% in 2009-2010. The

LAUSD dropout variable overestimates the dropout rate as a measure of people who never graduate

from any school in CA by a factor of about 1.5 because it includes both dropouts and students

who transferred to schools outside of the LAUSD. The actual LAUSD dropout rate, estimated by

the LAUSD, was 37.2 percent in 2010. The graduated variable is an overestimate of the actual

graduation rate because it is conditioned on entering the 12th grade.

4 Empirical Method

4.1 Estimating Teacher Value-Added

Let Si jt be a measure of student i’s test scores, behavior, or learning skills in academic-year t (in

teacher j’s class). For example, Si jt could be a standardized test score, an indicator for whether

the student was suspended, or a teacher’s assessment of a particular learning skill. The goal is to

estimate the effect of a teacher on several measures of students’ test scores, behavior, and learn-

ing skills. Recent research estimating teacher test-score value-added and its affects on long-term

outcomes has used slightly different estimation strategies (Chetty, Friedman, and Rockoff 2014b;

Jackson 2018; Rothstein 2017). Our approach combines elements of each, and we show in the

Appendix that the main results are robust across a range of estimation strategies.

For test-score value-added, we use the following estimation procedure. Although very similar,

a small but important adjustment is made when estimating behavior and learning-skills value-

added, which is discussed below. We construct value-added measures by first residualizing the

achievement measure, Si jt , by regressing it on a vector of controls, Xi jt , for lagged student achieve-

10

ment and the classroom environment using Equation (1). The baseline controls Xi jt include (1)

lags of a cubic polynomial of the student’s math test score, English test score, achievement GPA,

effort GPA, work and study habits GPA, and learning and social skills GPA; (2) lags of log days

absent, an indicator for suspensions, and an indicator for held back; (3) current English language

learner status; (4) a cubic polynomial of both class and grade level averages of lagged math test

score, English test score, achievement GPA, effort GPA work and study habits GPA, and learning

and social skills GPA; (5) class and grade level averages of lagged of log days absent, an indicator

for suspensions, an indicator for being held back, and English language learner status; (6) current

class size; and (7) grade fixed effects and year fixed effects. Each of the controls except current

English language learner status and current class size are interacted with grade fixed effects. In

middle and high school, our controls Xi jt are the same as in elementary school except we exclude

effort GPA, work and study habits GPA, and learning and social skills GPA controls for lack of

data.

Si jt = ΓXi jt + εi jt , (1)

εi jt = µ jt +αc + γit (2)

We assume the error term, εi jt , is an additively separable function of teacher quality (µ jt),

classroom shocks (αc), and student-year shocks (γit) as defined in Equation (2). This specification

of the error term, εi jt , is more flexible than one often used in the value-added literature that requires

a teacher’s quality be the same in each year this approach requires a stationarity assumption to

separately identify µ jt and αc.4

Let νi jt be the residualized student achievement computed as follows

νi jt = Si jt− Γ̂Xi jt . (3)

4See, assumption 1 of Chetty, Friedman, and Rockoff (2014a).

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The residualization purges Si jt of measures of the prior achievement of each student, each student’s

class, and each student’s grade. For middle and high school students we obtain the residuals using

only students who had one teacher per subject for English or math.

We then take the mean of the residuals, ν̄ jt =1N

∑Ni=1 νi jt , by year for each teacher j. This

provides an estimate of the teacher’s value-added score which is a measure of their ability to af-

fect student achievement in each year t. It is unbaised as long as certain teachers do not tend to

receive students with relatively better or worse than average unobserved achievement, specifically,

if E [αc + γit | j] = E [αc + γit ] (Chetty, Friedman, and Rockoff 2011). Although this is a strong as-

sumption, it is plausible in this context because the value-added model includes extensive controls

for students’ prior achievement and behavior in school that have been shown to account for most

student sorting in test-score value-added models (Bacher-Hicks, Kane, and Staiger 2014; Chetty,

Friedman, and Rockoff 2014a). To help alleviate concerns about student sorting based on unob-

servable components of student achievement, in section 5.5, we check for forecast bias, examine

the effect of teacher value-added on predicted outcomes as a placebo test, and perform a quasi-

experimental analysis that uses teachers switching grades and schools. We also show our estimates

are robust to including controls for tracking in middle and high school.

We then predict teacher quality in year t with the teacher’s estimated value-added scores in the

surrounding years using this equation ν̂ jt = ∑t+as=t−a ψ̂sν̄ js1[s 6= t], where a equals six years for test

scores and five years for behavior and learning skills. This approach measures of teacher quality

with data from the surrounding years to avoid biasing estimates of the long-term effects of teacher

quality on student outcomes (Jacob, Lefgren, and Sims 2010). Including year t in the prediction

would likely bias the long-term estimates because unobservables in year t that affect any dimension

of student performance may also affect both the estimated value-added measure in year t and the

long-term outcomes. We allow the weight placed on the value-added measure, ψ̂s, to vary by the

number of years before or after year t. We estimate the weights by minimizing the mean-squared

error of the difference between ν̄ jt and predictions of ν̄ jt made using the teacher value-added

measures the years before and after t. In particular, by solving the following minimization prob-

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lem, ψ ={ψt−a,...,ψt+a} ∑Jj(ν̄ jt−∑

t+as=t−a ψν̄ js1[s 6= t]

)2.5 This procedure produces leave-year-out,

jackknife, value-added predictions that allows teacher quality to change over time and shrinks the

value-added predictions to the mean through Bayesian shrinkage (Chetty, Friedman, and Rockoff

2014a).

We modify this procedure when we calculate non-test-score value-added measures by using

the lead of the achievement measure, Si j(t+1), as the outcome variable. This approach contrasts

with most of the non-test-score value-added literature that uses contemporaneous student outcomes

instead of outcomes in the next year, but is closely related to approaches used to calculate the value-

added of professors (Carrell and West 2010; Figlio, Schapiro, and Soter 2015). This approach

requires the main assumption – that teachers do not systematically receive students with relatively

better or worse unobserved achievement – holds for two years instead of just one. This assumption

in identical to that used in the value-added literature but is required for two years instead of one.

We use Si j(t+1) because using Si jt creates the potential for the non-test-score value-added mea-

sures to capture aspects of teacher behavior unrelated to teachers’ ability to affect students’ be-

havior or learning skills. For example, grades are likely affected not only by how much a teacher

helps a student learn and work diligently, but also by how strictly the teacher grades. Similarly,

suspensions are affected both by whether a teacher helps develop student behavior and how harshly

or leniently a teacher chooses to punish a student. These types of measurement error could lead to

biased estimates of the effect of teacher value-added on student outcomes.

A related concern is that teachers could directly affect long-term outcomes without affecting a

student’s behavior or learning skills. For example, if a teacher is more likely than other teachers

to recommend a student be held back, that student may be more likely to drop out of high school

even if the teacher actually has no effect on the student’s behavior or learning skills. This potential

direct effect could bias the effect of teacher value-added on long-term outcomes in the direction of

affecting long-term outcomes.

We remove bias from variation in teacher strictness (or leniency), and the direct effect of teach-

5See, Chetty, Friedman, and Rockoff (2014a) for additional details.

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ers on long-term student outcomes, by using the lead of the student achievement measure (i.e.,

achievement in year t +1, rather than the measure of student achievement in year t):

Si j(t+1) = ΓXi jt + εi jt . (4)

This approach introduces noise to our estimates because it partially captures the effect of the

teacher in year t +1, but removes systematic bias from teachers evaluating their own students. In

addition, using student achievement in year t +1 makes it more difficult for teachers to manipulate

their behavior or learning-skills value-added. It does not eliminate all measurement problems as

there are a limited number of year t + 1 teachers evaluating each teacher’s students and some

measures have an inherent subjectivity that is not fixed using this approach. However, we show

later in the paper that the GPA and learning skills measures, Si jt , are highly correlated with long-

term student outcomes, GPA and learning skills measures value-added affects outcomes after year

t +1. which suggests these variables measure meaningful skills.

In order for this approach to work, teachers must be able to exert some long-term influence on

their students behavior. One possible channel is directly by helping students develop noncognitive

skills (e.g., Cunha, Heckman, Schennach 2010; Heckman, Pinto, and Savelyev 2013; and Kraft

2019). Teachers may also affect parents behaviors and beliefs, e.g., about the value of attending

school or their child’s performance in school, and induce parents to reduce absences or help their

children with their homework.6

4.2 Estimating the Long-Term Effects of Teacher Value-Added

Once we have leave-year-out estimates of teacher quality, v̂ jt , we ask how having either a higher-

or lower-quality teacher along some dimension of teacher quality affects a student in the long

6For example, in CA 42-56% of elementary school parents were contacted by their school about attendance ina 6th month period (AdCouncil 2015). Nationally, 90% of parents of 3-5 graders report attending parent-teacherconferences (McQuiggani and Megra 2017) and one of the topics LAUSD suggests teachers inform parents about isthe benefit of better attendance (LAUSD New Teacher Resource Guide). There is also evidence from three RCTs thatcommunication from teachers or schools to parents substantially increases attendance (Cook et al 2017; Kraft andRogers 2015, Robinson et al. 2018; Rogers and Feller 2018)

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term. Let yi be a long-term outcome of interest such as whether a student is a high school dropout,

an indicator for taking the SAT, or a score on a test required for high school graduation. Let k

index the distinct leave-year-out value-added measures of test scores, behavior, or learning skills.

Elementary school students only have one teacher per year so we only have one measure of v̂ jkt

per student-year. Let ni jt be the number of classes the student, i, has with a teacher, j, in academic-

year, t. For a high school or middle school student, i, and subject s∈ {English, Math}, we construct

v̂sikt =

1

∑Jj=1 ni jt

∑Jj=1 ni jv̂s

i jkt , which is the mean value-added score weighted by the number of

classes the student has with the teacher.

We regress outcome, yi, on a number of value-added measures and our controls from equation

(1) and Xi jt which is the vector of baseline controls used in equation (1), which is defined in section

4.1, with the exception of variables that are not consistently available across years including work

and study habits GPA and learning and social skills GPA.7 The estimates of β̂k for each value-added

measure assess how each dimension of teacher quality affects the outcome of interest:

yi =K

∑k=1

βkν̂ jkt +ΓXi jt +ηi jt . (5)

We reduce the dimensionality of the estimates of teacher quality by constructing three indices

of the value-added variables. The first index is computed using teacher math and English test-

score value-added, which we call the test-score value-added, or θ̂ sjt . The second value-added index

is computed using value-added for suspensions, log days absent, GPA, and not progressing to the

next grade on time (i.e., held back), which we call the behavior value-added, or θ̂ bjt . The third

value-added index is computed using the value-added from effort GPA, work and study habits

GPA, and learning and social skills GPA, which we call learning-skills value-added, or θ̂ ljt . We

chose these three groups because they separate test scores from non-test scores, and because the

behavior value-added includes variables that are available for all grades, whereas the learning-skills

7In particular, the controls include lags of a cubic polynomial of the student’s math test score, English test score,GPA, and effort GPA; a cubic polynomials of lagged class and grade level means of each of those variables; and currentEnglish learner status and lagged log days absent, suspensions, and held back for the individual, the class average, andthe grade average. Each of these variables except English learner status is fully interacted with grade fixed effects anda control for class size is included.

15

value-added is available only for elementary school students. Our behavior value-added measures

for middle and high school students include measures for both their math and English teachers.

The indices are computed by summing the standardized value-added variables, recoded so each

has the same expected sign, and then standardizing the resulting index to be mean zero, standard

deviation one. In the Appendix, we show that the main results are robust to grouping GPA with

learning skills, using factor analysis to construct the three indices, and using exploratory factor

analysis to choose the factors and the factor load on each value-added measure.

We estimate the long-term effect of these value-added measures using the following specifica-

tion:

yi = βsθ̂

sjt +β

bθ̂

bjt +β

lθ̂

ljt +ΓXi jt +ηi jt . (6)

Where Xi jt is the vector of baseline controls used in equation (5). We also compare the estimates

from equation (6) with the estimates from a model that omits non-test-score value-added indices.

This comparison allows us to sign the bias from omitting non-test-score measures in papers that es-

timate the effect of teachers’ test-score value-added on long-term outcomes. If we find that β̂ s falls

when we move from a model that excludes θ̂ bjt and θ̂ l

jt to one that includes them, it suggests that

typical estimates of the long-term effects of test-score value-added are biased upward by omitted

measures of behavioral or noncognitive skills. Alternatively, if θ̂ bjt or θ̂ l

jt affect long-term out-

comes, and the estimate of β̂ s is unaffected by adding θ̂ bjt or θ̂ l

jt , the long-term effects of test-score

value-added may be unbiased, but estimates of the total effect of teachers on students is larger

than the effects found when using test-score value-added alone.8 We cluster the standard errors

at the high school level and show in Appendix Table A.1 that the standard errors not substantially

effected by bootstrapping over the estimation of the value-added scores described in Section 4.1

and estimation of the long-term effects using Equation (6).

Let tildes denote residualized student value-added indices, for example, θ̃ sjt = θ̂ s

jt − Γ̂X −

β̂ bθ̂ bjt− β̂ l θ̂ l

jt . To interpret the estimates in equation (6) as causal, we must assume cov(θ̃ sjt ,ηi jt) =

8We use the modal high school that students in each elementary school progress to so all students in a givenelementary school are in the same cluster.

16

cov(θ̃ bjt ,ηi jt) = cov(θ̃ l

jt ,ηi jt) = 0; the residualized leave-year-out predicted teacher value-added

indices and student unobservables that affect the outcome, yi, are uncorrelated. Although a strong

assumption, it does not rule out all sorting on unobservables. For example, suppose yi is an indi-

vidual’s SAT score and ηi jt is a scalar that captures the effect of an SAT preparation course (if any)

on an SAT score that is orthogonal to X . People who have unexplained variation in SAT prepa-

ration courses in high school could disproportionately be assigned to types of elementary school

teachers without biasing the results. It must only be the case that the sorting is unrelated to the ele-

mentary school teachers’ residualized value-added indices (Chetty, Friedman, and Rockoff 2011).

To help alleviate some of the concerns with this assumption, in section 5.5, we examine the effect

of teacher value-added on predicted outcomes as a placebo test and perform a quasi-experiment

analysis that uses teachers switching grades and schools.

In addition, there are reasons to believe that this approach is conservative. First, we find some-

what larger, although much less precisely estimated, effects using a quasi-experimental design that

uses variation in teachers switching between grades and schools. Second, we estimate smaller

effects than if we use the approach taken by Chetty, Friedman, and Rockoff (2014b).

We also extend this analysis in two ways. First, we examine the dynamic effects of a teacher

on student outcomes for years τ ∈ [0,1, ..,7]:

yi(t+τ) = βsθ̂

sjt +β

bθ̂

bjt +β

lθ̂

ljt +ΓXi jt +ηi jt . (7)

The model shows the extent to which the effect of teacher value-added on student outcomes persists

or fades over a number of years. Second, we assess the effects on long-term outcomes by grade to

see in which grades high-quality teachers have the most impact on students.

17

5 Results

5.1 Descriptive Results

5.1.1 Descriptive Relationships in Student Data

To assess whether multiple dimensions of teacher quality might matter for long-term outcomes,

we estimate the relationship between measures of student achievement, both with each other and

with long-term outcomes. Appendix Table A.2 shows bivariate correlations between each of the

measures of student achievement. English and math test scores are highly correlated. The rela-

tionship between test scores and students’ GPA, learning-skills GPA, and effort GPA is weaker

but the correlation still ranges from 0.45 to 0.68. The correlations of each of these variables with

attendance, days suspended, and being held back are substantially weaker, and suggests these vari-

ables largely capture different aspects of student achievement. These correlations suggest that test

scores, behavior, and learning skills are related, but that some room remains for them to have

an independent effect on long-term outcomes. Reducing the dimensionality of these variables by

separately computing test-score, behavior (i.e., attendance, days suspended, being held back, and

GPA), and learning-skills (i.e., learning-skills GPA and effort GPA) indices, as described in section

4.2, yields correlations between 0.46 and 0.55 (Table 2).

Next, we assess whether these measures of student achievement are related to long-term out-

comes, conditional on the same set of controls we use to compute the value-added measures. En-

glish and math test scores, GPA, learning-skills GPA, suspensions, log days absent, and held back

measured in grades 3 through 5 typically (but not always) have a statistically significant (or in one

case marginally significant) relationship with high school outcomes (Appendix Table A.3). After

reducing the dimensionality to the three indices of student achievement, test scores, behavior, and

learning skills measured in grades 3 through 5 nearly always have a statistically significant effect

on high school outcomes (Table 3). For many of the high school outcomes, behavior and learning

skills are as predictive of the outcome as test scores.

These results are consistent with test scores, behavior, and learning skills each independently

18

affecting long-term outcomes. However, despite the fact that we control for a wide range of mea-

sures of student achievement, these estimates may be biased because of unobservables. Conse-

quently, these results may not hold in situations in which there is exogenous variation in students’

test scores, behavior, and learning-skills achievement. To address this concern, we move to a

teacher value-added framework in which omitted variables are less likely to bias the results.

5.1.2 Descriptive Relationships in Teacher Value-Added Data

We compute teacher value-added as described in section 4.1. Appendix Table A.4 shows the re-

lationship between the value-added measures. English and math test-score value-added are highly

correlated. The correlations between test-score value-added and all other variables are much

weaker, but they are positively correlated with GPA, effort GPA, and learning-skills GPA, which

have correlations between 0.14 and 0.20. The GPA, effort GPA, and learning-skills GPA value-

added are highly correlated with each other. The three value-added measures of student behavior

– log absences, days suspended, and held back – are all weakly correlated with each other, test

scores, and GPA measures. These correlations suggest that math and English test-score measures

of teacher quality are closely related, as are GPA-based measures of teacher quality, whereas the

ability to influence student behavior relates less closely. Table 4 shows similar results. The correla-

tion between test-score value-added and behavior value-added is 0.15, the correlation of test-score

value-added with learning-skills value-added is 0.17, and the correlation of behavior value-added

with learning-skills value-added is 0.46.

5.2 Effects of Teacher Quality on Long-Term Outcomes

5.2.1 Single Value-Added Effects

Figures 2 through 4 show the effect of teachers’ test-score, behavior, and learning-skills value-

added individually on each high school outcome, conditional on the set of controls used to compute

value-added measures (also see Appendix Table A.5 for the results in this subsection reported in a

19

regression table). The plotted points show the relationship between the mean residualized outcome

and the mean residualized value-added variables (with the unconditional mean of the outcome and

value-added variables added back in) for 20 equally sized bins of teacher value-added measures.

The coefficients and standard errors reported in the figures are from an OLS regression, using the

micro data, of the outcome variable on the value-added variable, conditional on the same set of

controls.

Figure 2 shows that students with better teachers in grades 3 to 5, as measured by the test-score

value-added, score significantly higher on the SAT, have marginally significantly higher effort

GPAs and significantly higher cooperation GPAs, and score significantly higher on the high school

exit exams. We find no significant effects on dropping out of high school, taking the SAT, GPA,

being held back, log days absent, or being suspended. These results are consistent with the existing

literature that shows benefits in adulthood from higher test-score value-added teachers, although

research that demonstrates positive effects of elementary school teachers on high school outcomes

is rare (Rothstein 2017).

Figure 3 shows the effect of teachers’ behavior value-added on each outcome. We observe

at least marginally statistically significant effects in the expected direction on all of the outcome

variables except SAT score and English exit exam score. This indicates that in the absence of test-

score value-added, having a teacher with a higher behavior valued-added impacts the high school

outcomes of students in a meaningful way. Figure 4 shows the effect of teachers’ learning-skills

value-added on each outcome. We find less evidence of an effect than for the other two value-added

measures. The coefficient on the learning-skills value-added typically has the expected sign, but the

only marginally significant effect is on days suspended. The confidence intervals are sufficiently

small to reject moderate effects, but for several of the outcomes we cannot reject the effects of

he same size as the behavior value-added estimates. These results suggest that elementary school

teachers affect students’ long-term outcomes by increasing student achievement as measured by

both test-score and non-test-score data.

Comparing the magnitudes across the analyses, we tend to find that test-score value-added has

20

a large effect on outcomes that have more cognitive content than the behavior or learning-skills

value-added, whereas the pattern of results is reversed for outcomes that have more noncognitive

content. For example, having a teacher with a standard deviation higher test-score value-added

increases the math high school exit exam score by 0.023 standard deviations, whereas the increase

for behavior value-added is 0.014 standard deviations, and the statistically insignificant increase

for the learning-skills value-added is 0.004 standard deviations. However, the effect of having

a teacher with a standard deviation higher test-score value-added on days suspended is less than

0.001, whereas behavior and learning-skills value-added both reduce days suspended by 0.003

days, a 2 percent decrease.

The test-score value-added estimates appear to have two sets of potential nonlinear effects.

First, the effect of test-score value-added on all three GPA measures is positive until teachers be-

come above average, and then the relationship is, if anything, negative. Second, there is suggestive

evidence that the top vingtile or two of the test-score value-added distribution has a smaller effect

on several outcomes than would be predicted from the rest of the test score value-added distribu-

tion (Figure 2). Chetty, Friedman, and Rockoff (2014b) find a similar anomaly in their 4th-8th

grade test-score value-added results, and drop the top 1 percent of teachers because of evidence

of “test manipulation.” We leave those teachers in, although including them biases the effects of

test-score value-added toward zero if “test manipulation” exists. We find less evidence of non-

monotonicities for both behavior and learning-skills value-added, and outliers in the top vingtile

are less common. One explanation for this finding is that because non-test-score value-added mea-

sures are constructed using student achievement in year t + 1, teachers are unable to manipulate

their non-test-score value-added measure unless they influence the actions of their students’ teach-

ers in the subsequent year. Supporting this explanation, we find that when Figure 2 is recreated

with value-added measures using test scores in t +1 instead of year t (see Appendix Figure A.3),

these nonlinearities no longer exist.

Taken together, these results suggest that multiple components of teacher quality affect long-

term outcomes. Our findings also indicate that in situations in which no test-score data are avail-

21

able, but other administrative data such as grades, attendance, suspensions, and held back are

available, creating estimates of teacher quality that are associated with long-term benefits to stu-

dents is possible. Some evidence also suggests non-test-score value-added measures calculated

using our approach are less prone to manipulation by teachers, although they might begin to be

manipulated if used in high-stakes settings.

5.2.2 Multivariate Value-Added Effects

Now that we have found that two of the dimensions of teacher quality affect high school outcomes,

we can determine whether more than one value-added measure independently affects long-term

outcomes. Significant effects of more than one value-added measure would suggest that teacher

quality is multidimensional in a way that both matters for long-term outcomes and is measurable

using a value-added approach. In addition, this analysis informs the extent to which the long-term

effects of test score value-added measures are driven by teachers’ affect on behavior and learning

skills.

Table 5 shows the effect that each of the three elementary school value-added measures have on

high school outcomes in an OLS regression in which all three value-added measures are included

simultaneously along with the baseline controls (equation 6). Including behavior and learning-

skills value-added only slightly affects the coefficients on the test-score value-added measures.

For example, the coefficient in the SAT-score regression falls from 6.39 to 6.24 SAT points (or is

a constant 0.021 standard deviations), the coefficient in the math high school exit exam regression

falls from 0.023 to 0.022 standard deviations, and the coefficient in the high school GPA regression

falls from 0.004 to 0.002 GPA points. These results indicate that the long-term effects of test-score

value-added are likely not driven by teachers’ effects on students’ behavior and learning skills that

are correlated with test-score value-added.

The effects of behavior value-added on most outcomes is also not affected substantially by

conditioning on the test-score and learning-skills value-added. Behavior value-added picks up a

dimension of teacher quality that is largely unrelated to the other two value-added measures and

22

that matters for long-term outcomes. In addition, Appendix Table A.6 shows that there is no ev-

idence of an interactive effect between an elementary school teacher’s test-score and behavioral

value-added on students’ high school outcomes with coefficient estimates that suggest any interac-

tion effect is very close to zero.

Adding the other value-added measures does not affect the evidence for an independent effect

of teachers on long-term outcomes through learning skills. None of the coefficients on the learning-

skills value-added in Table 5 are statistically significant. For some of these outcomes, conditioning

on test score and behavior value-added results in the learning-skills value-added coefficients no

longer has the expected sign. The confidence intervals often, but do not always exclude effects of

the magnitude of the coefficients on behavior value-added.

This approach assumes teacher quality is not grade specific. We expect that if a competent of

teacher quality is grade specific we will tend to underestimate the long-term effect of teachers on

students because of measurement error in the independent variable of interest. To check this we

estimate grade-specific measures of teacher value-added. We find that while teacher value-added

measured for the same teacher in different grades is closely related, the slope is well below one

(Appendix Figure A.4). We then re-estimate the effect of teachers on long-term outcomes using

grade-specific measures of teacher value-added (Appendix Table A.7). The point estimates test-

score value-added increase by a factor of at least two for many of the outcomes and are newly

significant for drop out and achievement GPA. The point estimates for behavior-value generally

also increase (although not by as much) and are newly significant for SAT score and the English

exit exam, although, the effect on taking the SAT is no longer statistically significant. These result

suggest there is some grade-specific component of teacher ability and by assuming teacher quality

is constant across grades we tend to under estimate the effect of teachers on long-term outcomes.

Figure 5 shows how the value-added measures affect a number of outcomes that can be tracked

over time beginning in elementary school. The effect of test-score value-added on test scores shows

the expected pattern of results. Having a standard deviation higher test-score value-added teacher

has a large effect on math and English test scores in year zero that largely, but not completely, fades

23

out over the next seven years. Behavior value-added and learning-skills value-added show less ev-

idence of fade out, but our approach to constructing these variables should result in measures with

less fade out than test-score value-added. By measuring behavior and learning-skills value-added

using the effect of a teacher this year on student achievement in the next year, we are effectively

removing the first year of fade out from the estimates. In addition, because some of the student

achievement variables are grades, and students may be graded on a curve, seeing little effect of

behavior and learning-skills value-added on GPA measures in year zero would not be surprising.

The size of the long-term results can be interpreted using the cross-sectional relationship be-

tween test scores and earnings. Hanushek and Wossman (2008) find consistent evidence that in

the cross section, a standard deviation increase in test scores at the end of high school increases

earnings by 12 percent (Lazear 2003; Mulligan 1999; Murnane et al. 2000). Approximately the

same relationship holds between elementary and middle school test scores and earnings. Chetty,

Friedman, and Rockoff (2014b) show that direct estimates of a standard deviation increase in test-

score value-added for 4th-8th graders on earnings, and a back of the envelope estimate using the

cross-sectional relationship between primary school test scores and earnings, yield estimates of

the effect of teachers on earnings in the 1.3 to 1.5 percent range. We find effects of approximately

the same size or larger using the effect of teachers on contemporaneous test scores. If we instead

use the effect of having a standard deviation higher test-score value-added teacher in elementary

school on high school test scores, which ranges from 0.016 to 0.022 standard deviations, the esti-

mated increase in earnings would be 0.23 percent. This much smaller effect is likely driven by the

fade-out in the effect of teachers on test scores over time.

Combined, these results indicate that teacher quality is multidimensional in a way that matters

for long-term outcomes. Importantly, this multidimensionality can be measured using a combina-

tion of test scores and other data that schools routinely collect.

24

5.2.3 Multiple Hypothesis Testing

In this section we check whether the statistical significance of the results is robust to adjusting for

multiple hypothesis testing using several approaches. For the first approach, we simply constructed

indices of three families of outcomes: test scores (math and English CAHSEE)9, GPA (GPA, effort

GPA, cooperation GPA), and behaviors (took SAT, days suspended, log absences, held back). This

approach greatly reduces the number of hypotheses tested, makes use of the information in all our

outcomes, and it is much less computationally intensive than other methods. Appendix Table A.8

shows single factor results using the index outcomes. Test-score value-added significantly affects

the test score and GPA indices, behavior value-added significantly affects all three indices, and

learning skills has no significant effect on these outcomes. These results suggest the long-term

effects of teachers are robust to reducing the number of hypothesis tests. We also apply the index

approach to our main specification Table 5 where we include all three measures of teacher value-

added simultaneously, and find similar results except the effect of behavior value-added on the test

score index is not statistically significant (Appendix Table A.9).

Next, we use a number of methods to adjust for multiple hypothesis testing for each of our

independent variables of interest. We recomputed the p-values using the Bonferroni-Holm, Sidak-

Holm, and Romano-Wolf step-down methods along with the Westfall and Young (1993) algo-

rithm.10 We grouped the outcomes into three families of hypothesis tests: test scores, grades,

and all other outcomes. We then adjusted for multiple hypothesis testing within each family and

independent variable of interest. Appendix Table A.10 shows the results for each of these four

approaches (using 10,000 bootstrap draws where applicable). The p-values for test-score value-

added increase, however, the statistical significance of the results is generally robust to allowing

for multiple hypothesis testing. Although, effort GPA is no longer marginally significant under

most adjustments. Similarly, for behavior value-added the statistical significance of the results are

generally robust to these approaches with the exception that the effect on math test scores is now

9We do not include the SAT score because it would either made our sample much small or required us to imputea SAT score for a large fraction of students.

10We use code from Jones et al. (2018) and Clarke et al. (2019).

25

generally only marginally significant and held back is generally statistically insignificant

If instead of adjusting for multiple hypothesis tests by family, we instead adjust across all out-

comes, the statistical significance of test-score value-added is the same as using p-values adjusted

within family (Appendix Table A.11). For behavior value-added, the effect on math test scores

and cooperation GPA are no longer significant or marginally significant. The other outcomes that

were statistically significant remain at least marginally significant. We also re-computed p-values

for the index outcomes, allowing for multiple hypothesis testing across all three outcomes. The

statistical significance is unaffected except the p-values for test-score value-added on GPA are only

marginally significant for some adjustments (Appendix Table A.11).

Broadly these results show that we detect long-term, significant effects of having a higher

test-score and behavior value-added teacher and our results are not driven by multiple hypothesis

testing. However, the significance of some individual outcomes are not robust to these adjustments.

Notably the evidence that behavior-value added has long-term effects on test scores, conditional

on test score value-added, is weaker, although behavior value-added still significantly affects other

outcomes.

5.3 Teacher Selection Policies

Policies that use teachers’ test-score value-added to hire, fire, or incentivize teachers have been

widely criticized because making decisions using only one (potentially gameable) dimension of

teacher quality is considered unfair, or even counterproductive. However, the effect on long-term

outcomes of having higher test-score and behavior value-added teachers implies that policies that

shift the distribution of teacher quality upward in these dimensions benefit students. In comparison

to just using test-score value-added, we show that using multiple dimensions of teacher quality in

teacher-removal policies substantially improves the measurement of teacher quality and students’

long-term outcomes.

Figure 6 shows scatter plots of teacher quality as measured by value-added in a given year. The

dashed lines show the 5th percentile of teachers for a given value-added measure. The first panel

26

plots test-score and behavior value-added, and shows that although both dimensions of teacher

quality are positively correlated, the correlation is relatively weak, and some teachers who perform

poorly as measured by test-score value-added perform well on the behavior value-added dimen-

sion. For example, the majority of teachers who are in the bottom 5 percent of teachers as measured

by the test-score value-added are not in the bottom 5 percent of teachers as measured by the be-

havior value-added. Therefore, a linear combination of a teacher’s value-added measures might be

a better predictor of teacher quality and measure for teacher-removal policies.

One way to assess the value of using multiple measures of teacher quality is to ask to what ex-

tent students’ long-term outcomes could be improved under a policy that replaces a school district’s

bottom 5 percent of teachers with average teachers as measured by only test-score value-added ver-

sus different linear combinations of the three value-added measures. Panel A of Table 6 shows the

effect on a student’s high school outcomes of being assigned an average teacher instead of a teacher

in the bottom 5 percent of teachers as measured by a teacher’s true value-added (realized value-

added ex post, ν̄ jt). This panel shows the upper bound on the effects of the teacher-removal policy.

The simulation uses estimated effects of teacher value-added on high school outcomes (Figures 2

through 4 and Table 5) and the within-teacher correlations between the three teacher value-added

measures (Table 4). Standard errors for the estimated forecasts are shown in parentheses.

Each cell in row 1 shows the effect on students’ high school outcomes if their bottom 5 percent

test-score value-added elementary school teacher was replaced by an average teacher. For example,

the students that the policy would affect (about 5 percent) would see their SAT scores increase by

13 points and their high school GPA by 0.008 GPA points. Row 2 shows the effect on students’

high school outcomes if their bottom 5 percent behavior value-added elementary school teacher

was replaced by an average teacher. The benefits from using behavior value-added are comparable

to using test-score value-added, and in some cases, the benefits are larger.

Row 3 uses the average of teachers’ test-score and behavior value-added. When this combined

measure is used, students affected by the policy would see beneficial effects on all but one of the

high school outcomes. Row 5 shows the percent change in students’ outcomes if the replacement of

27

the bottom 5 percent of teachers is performed using the average of teachers’ test-score and behavior

value-added instead of just teachers’ test-score value-added. There is over a 100 percent increase

in the beneficial effects on students for dropping out of high school, taking the SAT, GPA, effort

GPA, days suspended, log absences, and held back. Importantly, these gains are accompanied by

only small decreases in English exit exam test scores and SAT scores.

Row 4 uses a maximization procedure to choose the optimal weights to be placed on a lin-

ear combination of teachers’ test-score, behavior, and learning-skills value-added to determine the

bottom 5 percent of teachers for the indicated outcome variable. The optimal weights vary depend-

ing on the outcome variable, so simultaneously improving all outcomes by the calculated amount

would not be possible. However, for most of the outcomes, a policy that uses the optimal weights

for a particular outcome only slightly outperforms a simple policy that places equal weights on

test-score and behavior value-added.

Panel B of Table 6 shows analogous results using teachers’ estimated value-added based on the

three previous years of student data. These results reflect the potential student gains if the teacher

removal policy were to be implemented for teachers who had taught for three years. Similar to

Panel A, student gains can be obtained if both test-score and behavior value-added are used to

make the teacher-removal decision. Because the autocorrelation between years for the behavior

measure is smaller than for the test-score measure (Appendix Figure A.5), the percent gain from

using both value-added measures instead of just the test-score value-added is smaller.

These results suggest that the dimensions of teacher quality captured by behavior value-added

are roughly as important for long-term outcomes as test-score value-added, and in combination,

can benefit student. Most of these benefits do not require new tests or assessments – only a new

use for data that schools already collect.

5.4 Which Behaviors Matter for Long-Term Outcomes?

Behavior value-added includes several weakly correlated value-added measures, some of which

may matter more than others for long-term outcomes. A straightforward way to assess which

28

variables matter most is to regress high school outcomes on the full set of value-added measures

that we use to construct the lower dimensional representation of teacher quality plus the usual set

of controls. We regress each outcome on each component of test score, behavior, and learning-

skills value-added on each outcome, conditional on our baseline controls so each cell in the table

is from a separate regression (like in Appendix Table A.5).

The test-score value-added measures have significant effects on SAT taking, the GPA measures

(English test-score value-added), and high school exit exams Appendix Table A.12. GPA VA

has significant effects on SAT taking and the English exit exam, and a similar magnitude but

insignificant effect on the math exit exam. Two other components of behavior value-added, absence

and suspension-value added, have significant effects on outcomes with both cognitive and non-

cognitive content. There is less evidence of an effect of held back value-added, but the held back

confidence interval general cannot reject effects of the magnitude of the absence or suspension-

value added measures. We generally do not see evidence of effect of the various components

of learning skills value-added. The coefficients typically have the expected sign, but the only

significant effect is of effort GPA on SAT taking. These estimates are somewhat less precise than,

e.g., the test score value added outcomes, and we often cannot reject effects of the magnitude of the

test score effects. Although the estimates tend to be slightly more precise than absences and days

suspended VA. If we instead include all value-added measures at once, we find broadly similar

effects except interpreting test score and GPA based measured results is more difficult because the

test score value-added measures are highly correlated, as are the GPA based value-added measures

(Appendix Table A.13). The results suggests that the behavior value-added results are driven

primarily by teachers’ effects on suspensions and absences.

Another way to illustrate this is to move GPA value-added from behavior value-added to

learning-skills value-added, and conduct the main analysis again. The new behavior value-added

constructed only from absences, suspension, and being held back has a significant or marginally

significant effect in the expected direction on six high school outcomes (Appendix Table A.14).

The new GPA-based value-added has a marginally significant effect on only taking the SAT. The

29

point estimates of the GPA-based value-added are often smaller than the significant effects of the

other value-added measures, and although the confidence intervals generally cannot reject effects

of the magnitude of the behavior value-added estimates.

These results suggest multiple dimensions through which teachers affect long-term student

outcomes, one that is closely related to increased performance on tests and others related to reduced

absences and suspensions. The abilities reflected in achievement GPA, effort GPA, and learning-

skills GPA matter for long-term outcomes, but the portion of these abilities that teachers are able

to affect is largely captured by test scores and the ability to keep the students in the classroom.

From a policy perspective, these results suggest that finding ways to keep children in the class-

room may have large benefits, e.g., the effect of reducing absence value-added on SAT scores is

about the same size as the sum of the effects of the two test score value-added measures. There

is also little of evidence that lack-of on-time progression in elementary school results in worse

high school outcomes, although our held-back value-added measure may be less informative as

our other value-added measures because most elementary school students progress on-time to the

next grade.

5.5 Checking for Bias in Long-Term Effects

We conduct four analyses to look for evidence of bias in the estimates of the long-term effects of

teachers. Consistent with Chetty, Friedman, and Rockoff (2014a) results for 4-8th grade test-score

value-added, most tests show no evidence of bias, and the magnitude of the bias in the remaining

tests is sufficiently small such that it does not substantially affect our conclusions.

First, we show that the value-added measures are forecast unbiased. Specifically, all but one

of the leave-year-out value-added variables cause an increase in the corresponding residualized

achievement variable that is statistically indistinguishable from one (Appendix Table A.15). Al-

though this is a weak test, failing this test would be problematic. Only math test scores are statisti-

cally different than one, for which a one unit increase in the math test-score value-added causes a

0.99 increase in math test scores. The confidence intervals are relatively tight for most outcomes,

30

although absences, suspensions, and held back are exceptions.

Second, we show that after conditioning on the main controls, students expected to perform bet-

ter in elementary school based on their twice-lagged values of achievement are largely not sorting

to higher value-added teachers. The estimated forecast bias from selection on student characteris-

tics is between−1.6 to 1.3 percent, which is smaller than Chetty, Friedman, and Rockoff’s (2014a)

point estimate of 2.2 percent for test-score value-added (Rothstein 2007). The forecast bias is only

marginally significant for GPA with a point estimates of 1.3 percent (Appendix Table A.15). An

analogous calculation using predicted high school outcomes from the twice-lagged values of the

control variables shows no evidence of upward bias. The only significant point estimates are for

behavior value-added, but each suggests that better students are sorted to worse teachers (Table 7).

Third, we aggregate these data to the school-grade-year level and estimate long-term effects

using quasi-experimental variation in teacher value-added caused by teachers switching between

grades or schools. The analysis removes variation in teacher value-added caused by students sort-

ing to teachers within a grade. Following Chetty, Friedman, and Rockoff (2014b), we regress

changes in school-grade-year high school outcomes on changes in the mean teacher value-added

weighted by the number of students.11 Appendix Table A.16 shows that the signs on the estimated

coefficients are generally consistent with the main results in Table 5, and the point estimates tend

to be larger. However, the estimates are much less precise. Despite this loss in statistical power, we

observe a significant effect of test-score value-added on math exit exams, and either significant or

marginally significant effects in the expected direction of behavior value-added for the three GPA

outcomes. As in the main table, learning-skills value-added is often wrong-signed and for some

outcomes are statistically significant.

Finally, we address the effects of tracking in middle and high school. During the period we use

to calculate value-added scores, students are tracked in math classes starting in 8th grade and they

have the opportunity to take more advanced English classes starting in 11th grade and students

11The sample is limited to cases in which we have value-added measures for all teachers in a given school-gradeyear in two consecutive years, to the subset of students for which we have both the long-term outcome variable and ateacher value-added measure, and to value-added measures that can be computed leaving out both year t and t−1.

31

may take elective English courses in both middle and high school. To the extent our interest is

estimating the benefit to an individual elementary school student of being shifted into a higher

value-added teacher’s classroom, as a parent might want to know, our estimates are not biased by

future tracking. Second, if a better teacher in elementary school results in a student being prepared

to take rigorous courses in high school or increases the quality of the students peers by directly

increasing the peers performance, we view that as an outcome of interest rather than a source of

bias. This is particularly true in the LAUSD where a large number of students struggle in rigorous

coursework and do not progress from 9th to 10th grade on time. However, some benefits of a

better elementary school teacher are zero sum, so our estimates may be too large for certain policy

experiments.

To help address this concern we re-estimate our main results from Table 5 after adding controls

for tracking including fixed effects for the number future middle and high school English electives,

English helper classes, English AP classes, math classes on each of the three math pathways, math

helper or below grade level classes, and AP math courses. Including these controls likely result in

us over-controlling for tracking. We find that the effect of a better teacher in elementary school on

most high school outcomes falls slightly and the statistical significance of the effects is generally

unaffected (Appendix Table A.17). For example, the effect of test-score value-added on math exit

exams falls from 0.22 to 0.20. Two exceptions to this pattern are the effect of test-score value-added

on LAUSD dropout which becomes marginally significant and the effect of test-score value-added

on effort GPA, which is no longer marginally significant. Additionally, Figure 5 provides some

evidence that tracking is not driving our results. Tracking starts 3-5 years after the current year for

math and 5-8 years after the current year for other occurs. There do not appear to be appreciable

changes in the effects of a better teacher in Figure 5 in the post-tracking years.

5.6 Robustness Checks

We conduct a number of robustness checks in which we use alternative approaches to constructing

the value-added indices, different specifications to estimate the long-term effects, and additional

32

high school outcomes. The results of the robustness checks are qualitatively consistent with the

main results.

Appendix Table A.18 reports the effect of teacher value-added on additional high school out-

comes such as graduating from the LAUSD if enrolled in the LAUSD in 12th grade, took the PSAT,

PSAT score, 11th grade English CST score (the last grade the CST is administered), 11th grade

math CST score, 8th grade science CST score, 10th grade science CST score, 8th grade social

studies CST score, 11 grade social studies CST score, world history CST score, and the number

of AP courses. We see significant or marginally significant effects of test-score value-added on

all outcomes except LAUSD graduation and took the PSAT. Test-score value-added affects test-

score outcomes by between 0.013 and 0.018 standard deviations, and does not vary noticeably by

subject. Having a high test-score value-added teacher in elementary school increases the long-

term performance across a number of subjects, not just English and math. The coefficients on

behavior value-added are typically the expected sign but are only marginally significant for one

outcome, whereas the coefficients on the learning-skills value-added are typically wrong-signed

and are statistically significant for one outcome. There is also little evidence that teacher value-

added in grades 3 through 5 affects whether students leave the school district in subsequent years

(Appendix Figure A.6).

We now show that the results are robust to a number of changes to our approach to computing

the value-added indices and long-term effects. The main results are larger and more often statisti-

cally significant if we follow Chetty, Friedman, and Rockoff (2014b) when computing value-added

measures by residualizing the achievement data using within-teacher variation in the controls (Ap-

pendix Table A.19). The effects are even larger if we use Chetty, Friedman, and Rockoff’s (2014b)

approach to computing long-term effects by residualizing the outcome variables using within-

teacher variation in the controls and then regressing the residualized high school outcomes on

teacher value-added with no controls (Appendix Table A.20). There is also more evidence of an

effect of learning skills value-added on long-term outcomes including significant effects in the

expected direction on dropout, and marginally significant effects on SAT score and English high

33

school exit exam score. The main results are essentially unchanged if we use factor analysis to

construct the three indices for teacher ability (Appendix Tables A.21 and A.22), and weaker for

the non-test score factor if we use exploratory factor analysis to construct two orthogonal factors

(Appendix Tables A.23 and A.24). Finally, we convert the test score value-added into deciles and

test score outcomes into percentiles, which means we use ordinal, rather than cardinal measures of

teacher value-added and test score outcomes. We continue to find significant effects of test score-

value-added on exit exam test scores and significant or marginally significant effects of behavior

value-added on took SAT, math test scores, absences, and held back (Appendix Table A.25).

Appendix Table A.26 shows that if we remove behavior value-added from the main analysis,

learning-skills value-added no longer has one wrong-signed coefficient. This suggests that part of

the reason for the unintuitive results for learning-skills value-added is that behavior value-added

and learning-skills value-added are moderately correlated.

Another concern with our interpretations of the results is that the effect of teacher value-added

on long-term outcomes could be driven by the direct effect of teacher actions like suspensions or

retaining a student on both teacher value-added and the student’s long-term outcomes. While this

is likely happening to some degree, it is unlikely this effect is large enough to influence our main

results because both types of events are rare for elementary school students. In any given, year

only about 0.7% of elementary school students are held back and the average elementary school

student is suspended for 0.05 days (Table 1). To check the magnitude of this effect we replicate our

main table excluding students who were held back or suspended. We find only slight changes in

our results (Appendix Table A.27). The magnitudes are very similar to the baseline results in Table

5, and there are only minor changes in the statistical significance of the coefficients of interest.

This suggests the direct effect of suspension and grade retention plays a small role in our estimated

results.

We also test the sensitivity of our results to using year t + 1 measures of the behavior and

learning skills to estimate behavior and learning skills value-added. First we re-estimate test-score

value-added using year t + 1 test scores, and then we replicate our main results from Table 5

34

using test-score, behavior and learning skills value-added all estimated using year t +1 measures.

Generally, we find larger long-term effects of test-score value-added estimated using t+1 data than

year t data. There are also slight decreases in the effect of behavior value-added on some long-

term outcomes (Appendix Table A.28). We also re-estimate behavior and learning skills value-

added using year t data than year t + 1. The tests score VA measures are essentially unchanged

while the estimates of behavior value-added are generally smaller and the effects on SAT taking,

math test scores, and grade retention are no-longer statistically significant. Although, there are

still significant or marginally significant results on the three GPA measures and log days absent.

Interestingly, the learning skills estimates tend to be slightly larger in absolute value and the results

suggests higher learning skills value added leads to significantly or marginally significantly worse

results for six high school outcomes (Appendix Table A.29).

We also check if our results are sensitive to using GPA measured in z-scores rather than grade

points both when computing value-added scores and as long-term outcomes (Appendix Table

A.30). Converting the GPA measures to z-scores before estimating teacher VA had no effect on the

relationship between the teacher value-added indices and the GPA outcomes in points. The statisti-

cal significance GPA outcomes in SD units is the same as when they are measured in grade points.

The change in magnitudes is what would be expected give the SD of the high school outcomes

reported in Table 1.

6 Applications of Non-Test-Score Measures

We take the general approach from the last section to demonstrate that non-test-score measures

of achievement are useful for answering additional questions related to the effects of teachers on

students. We first examine teacher effects over the educational life cycle, and ask to what extent

there could be long-term gains from moving high value-added teachers between grades and what

are the cumulative benefits from increasing teacher quality. We then take the approach used to

construct non-test-score value-added measures to compute GPA value-added for specific subjects

35

in order to test the long-term value of having a better teacher in different subjects. This analysis

could also be interpreted as suggesting how long-term student performance could be obtained from

reallocating time between subjects.

6.1 Implications for Long-Term Effects of Teacher Quality over the Educa-

tional Life Cycle

The approach in this analysis is to compute the test-score and behavior value-added for teachers in

grades 3 through 12, and ask how having a standard deviation better teacher in each grade affects

long-term outcomes as measured in 11th or 12th grade.12 We do not compute learning-skills value-

added because we do not have learning-skills data for middle and high school students. Previous

work on long-term teacher effects by grade has estimated the effects of test-score value-added

for grades 4 through 8 (Chetty, Friedman, and Rockoff 2014b), and test-score and non-test-score

value-added for grade 9 in North Carolina (Jackson 2018).

Figure 7 reports the results of this analysis for outcome variables measured as late as possible

in a student’s career. In each graph, we also report the sum of the coefficients across all grades.

With some assumptions, particularly no tracking of students and no diminishing returns to having

consecutive high quality teachers (Kinsler 2016), this sum reflects an upper bound on the cumu-

lative effect of having a standard deviation higher test-score or behavior value-added teacher in

each grade from 3rd through 12th grade. If tracking students plays a large role, or if diminishing

returns exist, this sum overestimates the cumulative effect and is an upper bound. However, Chetty,

Friedman, and Rockoff (2014b) find evidence for only a small amount of tracking, conditional on

controls for lagged achievement in grades 4-8, and we include a robustness check that directly

controls for tracking.

We find that having a standard deviation higher test-score teacher in grades 3 through 12 has a

beneficial effect on taking the SAT, SAT scores, and math and English test scores. The cumulative

12We cannot compute teacher value-added in 12th grade, so value-added measures for teachers in 12th grade useestimates of teacher quality in earlier grades.

36

effect for each of these outcomes is quite large. Using the cross-sectional relationship between test

scores and earnings, and the cumulative effects on math and English tests scores, having a standard

deviation higher test-score value-added teacher in each grade increases a student’s adult earnings

by 2.7 to 5.2 percent.

We also find that having a standard deviation higher behavior value-added teacher in grades 3

through 12 has a large beneficial effect on dropping out of high school, graduation, taking the SAT,

the three GPA measures, absences, suspensions, and grade retention. For example, the cumulative

effects suggest that having a standard deviation higher behavior value-added teacher in each grade

decreases the likelihood of dropping out of high school by 9.0 percentage points. Once adjusted for

dropping out of high school being over estimated due to students leaving the LASUD, this effect

is still a 5.9 percentage point decrease.

We also show the magnitude of the cumulative effects of better teachers in Figure 7 is not is

driven by tracking. We re-estimate the value-added scores for middle and high school after adding

controls for contemporaneous tracking. Then we re-estimate the grade-by-grade long-term effects

in the elementary school controlling for future tracking. For middle and high school grades we

use the value-added measures estimated with tracking controls, and when estimating the effect of

teacher-value added on longer term outcomes we include controls for contemporaneous tracking

(Appendix Figure A.7). We control for future tracking using the same approach as in Appendix

Table A.17, and control for contemporaneous tracking with fixed effects for the number of English

elective, English helper classes, English AP classes, classes on each of the three math pathways,

math helper or below grade level classes, and AP math classes in each year. The effects of a

better teacher on each outcome are generally smaller than in Figure 7, but only slightly and our

conclusions are unaffected. For example, cumulative effects on SAT taking fall from 8.1 and

8.4 percentage points for test score and behavior value-added, to 7.8 and 7.7 percentage points,

respectively.

We re-estimate the cumulative effects using an alternative approach where we compute the

average test-score and behavior-value added for each student’s teachers across grades 4-11, then

37

regress the outcomes reported in Figure 7 on the average test-score and behavior-value added and

controls measured in fourth grade.13 The support of the distribution includes plus or minus one

standard deviation in teacher value-added (Appendix Figure A.8). The effect of behavior value-

added on long-term outcomes are systematically smaller than in Figure 7, but they still imply

substantial effects of providing students with better behavior value-added teachers for a number of

outcomes (Appendix Table A.31). The effect of test-score value-added on long-term outcomes is

approximately as large and for some outcomes notably larger than the estimates reported in Figure

7. The outcomes where test-score value-added increases the most tend to be ones where behavior

value-added decreases substantially, which suggests this empirical approach puts more weight on

better test-score value-added teachers than behavior value-added teachers.

Since we have data on all grades 3 to 12, we can also examine whether having a high value-

added elementary school teacher or high school teacher has a larger long-term effect on student

outcomes under the strong assumption that a one standard deviation change in teacher value-added

induces the same amount of learning in each grade. Models of human capital formation in which

past human capital production is complementary to current human capital production suggests that

having a high value-added elementary school teacher is more important (Kinsler 2016). Alterna-

tively, the substantial fade out we observe in the effect of teacher value-added suggests that high

school teachers will have a larger effect on student outcomes.

The results generally show that having a high value-added English and math teacher in middle

school or high school has a bigger impact on long-term outcomes than having a high value-added

elementary school teacher. This pattern of results is especially clear for dropping out of high

school, test scores, the GPA measures, absences, suspensions, and grade retention. For example,

having a one standard deviation higher behavior value-added teacher has little effect on whether

a student drops out of high school in grades 3 to 5, but reduces the likelihood of dropping out by

about one percentage point per year in grades 6 to 12. Exceptions exist, notably for taking the SAT,

but the pattern of results is fairly clear. The strength of the middle school and high school effects

13We start in fourth grade rather than third grade because it substantially increases our sample size. We include allstudents with at least seven observations of teacher value-added in the eight years from grade four to eleven.

38

is somewhat surprising because we only calculate value-added for English and math teachers, with

whom a student spends less than half of her school day, whereas in elementary school, the value-

added measures are calculated using a classroom teacher with whom students spend much more

time. However, these results should be interpreted with an abundance of caution since common

cardinality of the VA measures across grades is difficult to fully defend given that achievement

measures are standardized within each grade-year. It is possible that one standard deviation in the

VA distribution at later grades represents a larger magnitude shift in an underlying distribution of

skill relative to a one standard deviation shift in earlier grades.

6.2 Measuring Teacher Quality in Untested Subjects

A significant shortcoming of the test-score value-added framework is that a test must be adminis-

tered in a subject to measure a teacher’s quality in that subject. Consequently, we cannot evaluate

teachers using value-added measures in subjects or grades that are untested, or compare the im-

portance of high quality teachers across untested subjects. We extend the approach for calculating

non-test-score value-added described in section 4.1 to compute value-added measures for elemen-

tary school teachers by subject using students’ grades in each subject. We measure a teacher’s

quality using the grade each student receives in a subject in year t +1, controlling for the baseline

controls from year t−1.

Appendix Table A.32 shows long-term student outcomes regressed on students’ grades and

the standard set of controls. Better grades in virtually all subjects improve students’ long-term

outcomes. Two exceptions are speaking and PE, which indicate that students who perform better

in speaking or PE perform worse in high school, even conditional on their grades in other subjects

and prior achievement. However, these estimates may not be detecting the true effect of ability in

a particular subject, but unobserved characteristics associated with both elementary school grades

and high school outcomes.

Table 8 reports the results after redoing this analysis using teacher value-added for each sub-

ject. Teachers who excel at teaching reading and health have students who perform better in high

39

school across several measures. The effects for math-GPA value-added are subject specific. The

effect is only positive and marginally significant for math high school exit exam scores. Unex-

pectedly, students with better elementary school math teachers are more likely to be held back in

high school. The effects of reading-GPA value-added are more widespread, with significant or

marginally significant effects on dropout, taking the SAT, SAT score, and both math and English

high school exit exam scores. Writing-GPA value-added significantly affects the probability of

being held back. Health-GPA value-added significantly affects for taking the SAT, cooperation

GPA, and is marginally significant for both math and English high school exit exam scores. Social

studies has positive and marginally significant effects on two of the grade measures.

Speaking-GPA value-added has a positive and significant effect on dropout, and negative and

significant or marginally significant effects on SAT scores, both high school exit exam measures,

and all three GPA measures. Perhaps talking in class is not well rewarded in high school. The

coefficients on PE, Arts, and science are also generally suggest negative effects on students but in

most cases are not statistically significant.

The confidence intervals on the statistically insignificant estimates are such that they often

cannot reject effects of the magnitude of reading or health GPA on the outcomes. Although for

example, the effect of reading GPA on taking the SAT and test scores is outside the confidence

interval for many outcomes as is the effect. Similarly, the negative effects on students of the

magnitude of speaking value-added on e.g., dropout and SAT score, are often rejected by the

confidence intervals of the other value added measures.

These results broadly support the traditional view that reading is a building-block skills that has

long-term benefits. The health results are unexpected, suggesting that health knowledge at a young

age could have long-term benefits, though this explanation should be interpreted with caution.

Besides the negative effect of speaking, we find relatively little evidence of negative effects of

other subjects. These results suggest that elementary schools could potentially create long-term

benefits for students by hiring and retaining strong reading teachers and focusing more of their

time on teaching reading.

40

7 Conclusion

The results demonstrate that teacher quality is multidimensional. We show that teachers’ test-score

value-added has significant effects on long-term outcomes, and that adding controls for behavior

and learning-skills value-added does not influence the estimated effects. This finding indicates the

long-term effects of having a high test-score value-added teacher may not be biased upward by

omitting measures of behavior or learning-skills value-added.

We also find that a teacher value-added measure that combines the teacher valued-added effects

on GPA, absences, suspensions, and grade retention affects many high school outcomes. These ef-

fects are similar in magnitude to test-score value-added. We find little evidence that learning-skills

value-added individually effects high school outcomes. The second dimension of teacher quality

is only weakly correlated with test-score value-added, and allows for a substantial enhancement

in the measurement of teacher quality. For example, a policy that uses both dimensions and three

years of data to identify the bottom 5 percent of teachers and replaces them with average teachers

increases the efficacy of the policy by over 50 percent versus a policy that uses only test-score

value-added for dropout rates, the likelihood of taking the SAT, GPA, effort GPA, absences, and

being held back. Despite substantial gains in many areas, high school test scores experience only

minimal declines.

We then demonstrate that this value-added framework can be extended to analyze effects by

grade and all elementary school subjects. We find that cumulative effect of better elementary,

middle, and high school teachers is large, even after controlling for tracking. We also show that

teachers who are relatively better at teaching reading and health improve their elementary school

students’ high school outcomes, whereas teachers who are better at teaching speaking worsen

them. Teaching reading may have long-term benefits for students, which suggests schools should

focus on increasing teaching quality in that area.

Overall, this paper shows the multifaceted role that teachers play in influencing the outcomes of

their students. In addition to benefiting students through increasing performance on tests, teachers

meaningfully impact students long-term outcomes through additional channels.

41

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Murnane, Richard J., John B. Willett, Yves Duhaldeborde, and John H. Tyler. 2000. “How Im-portant Are the Cognitive Skills of Teenagers in Predicting Subsequent Earnings.” Journalof Policy Analysis and Management, 19(4): 547-568.

National Council on Teacher Quality. 2015. “2015 State Teacher Policy Yearbook: NationalSummary.” Technical Report.

Neal, Derek. 2011. “The Design of Performance Pay in Education.” In Handbook of the Eco-nomics of Education Volume 4, edited by Eric A. Hanushek, Stephen Machin, and LudgerWoessmann, 495-550. Amsterdam: North-Holland, Elsevier.

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Romano, Joseph P., and Michael Wolf. 2016. "Efficient computation of adjusted p-values forresampling-based stepdown multiple testing." Statistics & Probability Letters, 113: 38-40.

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44

Figure 1: LAUSD Elementary School Progress Report

Note: The figure displays a blank copy of an LAUSD elementary school progress report. Each row labels the academic subject, work and studyhabits, or learning and social skill each student is graded on by their teacher. Columns 1, 2, and 3 correspond to each of the three trimesters studentsreceive a grade. For the academic subjects, the AC column stands for achievement scores and the EF column stands for effort scores. For allacademic subjects, work and study habits, and learning and social skills, students receive a grade ranging from 1 (the poorest performing) to 4 (thebest performing).

45

Figure 2: Effect of Teacher Test-Score Value-Added on High School Outcomes

Note: The sample includes students in grades 3-5 who attended high school in the LAUSD. The unit of observation is a student-academic year.Figure 2 shows binned scatter plots of residualized high school outcome variables and normalized teacher test-score value-added for grades 3, 4, or5. We construct these plots by first residualizing the outcome and teacher value-added variables using the controls shown in equation (6). We thenplot the mean values of both variables in 20 equally sized bins. Lastly, we add back the unconditional mean of both variables. We also plot the bestlinear fit estimated prior to binning the data, and report its slope coefficient and standard error, clustered at the modal high school level. *** p <0.01, ** p < 0.05, and * p < 0.10.

46

Figure 3: Effect of Teacher Behavior Value-Added on High School Outcomes

Note: The sample includes students in grades 3-5 who attended high school in the LAUSD. The unit of observation is a student-academic year.Figure 3 shows binned scatter plots of residualized high school outcome variables and normalized teacher-behavior value-added for grades 3, 4, or5. We construct these plots by first residualizing the outcome and teacher value-added variables using the controls shown in equation (6). We thenplot the mean values of both variables in 20 equally sized bins. Lastly, we add back the unconditional mean of both variables. We also plot the bestlinear fit estimated prior to binning the data, and report its slope coefficient and standard error, clustered at the modal high school level. *** p <0.01, ** p < 0.05, and * p < 0.10.

47

Figure 4: Effect of Teacher Learning-Skills Value-Added on High School Outcomes

Note: The sample includes students in grades 3-5 who attended high school in the LAUSD. The unit of observation is a student-academic year.Figure 4 shows binned scatter plots of residualized high school outcome variables and normalized teacher learning-skills value-added for grades 3,4, or 5. We construct these plots by first residualizing the outcome and teacher value-added variables using the controls shown in equation (6). Wethen plot the mean values of both variables in 20 equally sized bins. Lastly, we add back the unconditional mean of both variables. We also plot thebest linear fit estimated prior to binning the data, and report its slope coefficient and standard error, clustered at the modal high school level. *** p< 0.01, ** p < 0.05, and * p < 0.10.

48

Figure 5: Dynamic Effects of Test-Score, Behavior, and Learning-Skills Teacher Value-Added

Note: Unit of observation is a student-academic year. Each plot shows the effect of test-score, behavior, and learning-skills value-added of teachersin grades 3, 4 or 5 on student outcomes in the concurrent year (the year a student was in a teacher’s classroom) and future years (the years after astudent was in a teacher’s classroom). The estimated effects are obtained by regressing leads of outcome variables on teacher test-score, behavior,and learning-skills value-added and the baseline controls as specified in equation (7). The coefficients on test-score, behavior, and learning-skillsvalue-added are plotted along with 95 percent confidence intervals, with standard errors clustered at the modal high school level.

49

Figure 6: Two-Dimensional Cross Teacher Value-Added Plots

Note: The first scatter plot shows a plot of elementary school teachers’ annual, normalized test-score and behavior value-added within three standarddeviations of the mean. The two dashed lines show the cutoffs for the 5th percentile of the test-score and behavior teacher value-added, respectively.The second and third scatter plots are constructed analogously for test-score value-added versus learning-skills value-added, and behavior value-added versus learning-skills value-added, respectively.

50

Figure 7: Effects of Test-Score and Behavior Teacher Value-Added by Grade

Note: The sample includes students in grades 3-5 who attended high school in the LAUSD. The unit of observation is a student-academic year. Thefigure shows plots of the effect of test-score and behavior teacher value-added on high school outcomes by the grade level of the student. The plottedcoefficients and standard errors (clustered at the modal high school level) are from a regression of the high school outcome variable on test-scoreand behavior teacher value-added, and the vector of controls for high school students specified in section 4.1, estimated separately for each grade.

51

Table 1: Summary Statistics

Variable MeanStandardDeviation

Observations

Panel A: Grades 3 - 5Math CST Score 0.01 1.00 891,643English CST Score 0.00 1.00 891,751GPA 2.88 0.42 861,977Effort GPA 3.14 0.46 861,977Learning Skills GPA 3.10 0.58 614,532Fraction of Days Absent 3.9% 858,308Days Suspended 0.05 0.38 906,193Held Back 0.7% 837,401English Learner 42.0% 906,193Panel B: High School OutcomesLAUSD Dropout 54.6% 333,513Took SAT 50.5% 249,436SAT Score 1,330 298 145,265GPA 2.25 0.96 536,868Effort GPA 2.12 0.52 476,548Cooperation GPA 2.33 0.45 476,548Math CAHSEE Score 0.07 1.01 331,266English CAHSEE Score 0.08 0.98 329,980Days Suspended 0.18 0.83 588,273Fraction of Days Absent 7.8% 542,959Held Back a Grade 29.0% 449,533Graduated if Entered 12th Grade 88.6% 190,278Took PSAT 68.9% 470,703PSAT Score 1,110 248 348,992Math CST Score -0.05 0.99 124,044English CST Score 0.00 0.99 135,769Grade 8 Science CST Score 0.02 1.00 599,880Grade 10 Science CST Score 0.09 1.00 296,069Grade 8 Social Science CST Score 0.03 1.00 548,439Grade 11 Social Science CST Score 0.07 0.99 160,483World History CST Score 0.06 0.98 270,403Number of AP Courses 0.73 1.70 588,273

Note: The sample includes students in grades 3-5 who attended the LAUSD. The unit of observation is a student-academic year. Panel A reportssummary statistics for all LAUSD students in grades 3-5 from 2004 to 2010. Panel B reports high school summary statistics for all LAUSD studentswho were in grades 3-5 from 2004 to 2010 and attend high school in the LAUSD. Elementary school GPA, effort GPA, and learning-skills GPA areon a 4-point scale. GPA in high school is on a 4-point scale, and effort GPA and cooperation GPA in high school are on a 3-point scale. All testscores except the SAT and PSAT are normalized at the grade-year level. Both the SAT score and PSAT score are on a 600-2400 scale. The LAUSDDropout variable is the fraction of students who enrolled in an LAUSD school in 9th grade and did not graduate from the LAUSD within five years.The Graduated if Entered 12th Grade variable shows the fraction of students who enrolled in an LAUSD school in 12th grade and graduated fromthe LAUSD.

52

Table 2: Correlation of Elementary School Student Achievement Measures

Measure Test Scores BehaviorLearning

SkillsTest Scores 1Behavior 0.463 1Learning Skills 0.532 0.550 1

Note: The sample includes students in grades 3-5 who attended the LAUSD. The unit of observation is a student-academic year. Table 2 reportsthe correlations between the three measures of student achievement for grades 3-5. Each of the three measures of student achievement are equallyweighted indices. The test-score index is computed using the students’ normalized math and English test scores. The behavior index is computedusing students’ GPA, suspensions, log days absent, and not progressing to the next grade on time (held back). The learning-skills index is computedusing students’ effort GPA, work and study habit GPA, and learning and social skills GPA.

53

Tabl

e3:

Rel

atio

nshi

pbe

twee

nE

lem

enta

rySc

hool

Ach

ieve

men

tand

Hig

hSc

hool

Out

com

es

Mea

sure

LA

USD

Dro

pout

Took

SAT

SAT

Scor

eG

PAE

ffor

tGPA

Coo

pera

tion

GPA

Mat

h

CA

HSE

E

Eng

lish

CA

HSE

E

Day

s

Susp

ende

d

Log

Abs

ence

sH

eld

Bac

k

Test

Scor

es-0

.032

***

0.09

1***

143.

271*

**0.

208*

**0.

103*

**0.

089*

**0.

472*

**0.

398*

**-0

.009

***

-0.1

12**

*-0

.052

***

(0.0

03)

(0.0

04)

(2.6

44)

(0.0

04)

(0.0

02)

(0.0

02)

(0.0

06)

(0.0

06)

(0.0

02)

(0.0

05)

(0.0

03)

Beh

avio

r-0

.020

***

0.02

8***

10.8

26**

*0.

062*

**0.

032*

**0.

030*

**0.

028*

**0.

020*

**-0

.028

***

-0.1

30**

*-0

.017

***

(0.0

02)

(0.0

03)

(1.5

54)

(0.0

04)

(0.0

02)

(0.0

02)

(0.0

03)

(0.0

03)

(0.0

03)

(0.0

06)

(0.0

02)

Lea

rnin

gSk

ills

-0.0

40**

*0.

068*

**-2

.393

*0.

228*

**0.

133*

**0.

121*

**0.

045*

**0.

064*

**-0

.045

***

-0.0

62**

*-0

.061

***

(0.0

02)

(0.0

03)

(1.2

90)

(0.0

03)

(0.0

02)

(0.0

02)

(0.0

03)

(0.0

03)

(0.0

03)

(0.0

04)

(0.0

02)

Obs

erva

tions

134,

356

100,

691

59,5

8232

1,23

625

1,46

025

1,46

016

0,38

515

9,91

134

3,72

030

2,90

224

0,20

4

R-s

quar

ed0.

421

0.17

90.

686

0.32

10.

315

0.32

60.

572

0.57

70.

045

0.28

10.

134

Not

e:T

hesa

mpl

ein

clud

esst

uden

tsin

grad

es3-

5w

hoat

tend

edhi

ghsc

hool

inth

eL

AU

SD.T

heun

itof

obse

rvat

ion

isa

stud

ent-

acad

emic

year

.Thi

sta

ble

repo

rts

the

pred

ictiv

eef

fect

ofa

stan

dard

devi

atio

nin

crea

sein

each

ofth

eth

ree

mea

sure

sof

stud

enta

chie

vem

ent(

see

Tabl

e2)

ingr

ades

3-5

onhi

ghsc

hool

stud

ento

utco

mes

.Spe

cific

ally

,eac

hco

lum

nof

the

tabl

ere

port

sth

eco

effic

ient

son

each

ofth

eth

ree

achi

evem

entm

easu

res

ofst

uden

tsfr

oman

OL

Sre

gres

sion

ofth

ehi

ghsc

hool

stud

ento

utco

me

onth

est

uden

ts’t

hree

achi

evem

entm

easu

res

ingr

ades

3,4,

or5,

alon

gw

ithth

eba

selin

eco

ntro

lsde

scri

bed

inse

ctio

n4.

1.T

heba

selin

eco

ntro

lsin

clud

ela

gsof

acu

bic

poly

nom

ialo

fthe

stud

ent’s

mat

hte

stsc

ore,

Eng

lish

test

scor

e,G

PA,a

ndef

fort

GPA

;acu

bic

poly

nom

ials

ofla

gged

clas

san

dgr

ade

leve

lmea

nsof

each

ofth

ose

vari

able

s;an

dcu

rren

tEng

lish

lear

ner

stat

usan

dla

gged

log

days

abse

nt,s

uspe

nsio

ns,a

ndhe

ldba

ckfo

rth

ein

divi

dual

,the

clas

sav

erag

e,an

dth

egr

ade

aver

age.

Eac

hof

thes

eva

riab

les

exce

ptE

nglis

hle

arne

rsta

tus

isfu

llyin

tera

cted

with

grad

efix

edef

fect

san

da

cont

rolf

orcl

ass

size

isin

clud

ed.

Stan

dard

erro

rscl

uste

red

atth

em

odal

high

scho

olle

vela

rere

port

edin

pare

nthe

ses.

***

p<

0.01

, **

p<

0.05

,and

*p

<0.

10.

54

Table 4: Correlation of Elementary School Teacher Value-Added Measures

Grades 3-5 VATest Score

VABehavior

VALearningSkills VA

Test Score VA 1Behavior VA 0.145 1Learning Skills VA 0.174 0.459 1

Note: The sample includes students in grades 3-5 who attended the LAUSD. The unit of observation is a student-academic year. Table 4 reports thecorrelations between the three measures of teacher value-added for grades 3-5. Each of the three measures are equally weighted indices. The test-score value-added is computed using teachers’ value-added for math and English test scores. The behavior value-added is computed using teachers’value-added for GPA, suspensions, log days absent, and not progressing to the next grade on time (held back). The learning-skills value-added iscomputed using teachers’ value-added for effort GPA, work and study habit GPA, and learning and social skills GPA.

55

Tabl

e5:

Eff

ecto

fEle

men

tary

Scho

olTe

ache

rVal

ue-A

dded

onH

igh

Scho

olO

utco

mes

Pool

edG

rade

s3-

5VA

LA

USD

Dro

pout

Took

SAT

SAT

Scor

eG

PAE

ffor

tGPA

Coo

pera

tion

GPA

Mat

h

CA

HSE

E

Eng

lish

CA

HSE

E

Day

s

Susp

ende

d

Log

Abs

ence

sH

eld

Bac

k

Test

Scor

eVA

-0.0

02-0

.002

6.23

7***

0.00

20.

003*

0.00

5***

0.02

2***

0.01

6***

0.00

10.

001

0.00

1

(0.0

02)

(0.0

02)

(1.2

62)

(0.0

02)

(0.0

01)

(0.0

01)

(0.0

03)

(0.0

02)

(0.0

01)

(0.0

03)

(0.0

01)

Beh

avio

rVA

-0.0

030.

010*

**1.

955

0.01

3***

0.00

7***

0.00

5**

0.01

3**

0.00

4-0

.003

-0.0

16**

*-0

.006

**

(0.0

02)

(0.0

03)

(2.1

39)

(0.0

04)

(0.0

03)

(0.0

02)

(0.0

06)

(0.0

05)

(0.0

02)

(0.0

05)

(0.0

03)

Lea

rnin

gSk

ills

VA-0

.000

-0.0

01-0

.547

-0.0

02-0

.003

-0.0

03-0

.005

0.00

1-0

.002

0.00

20.

001

(0.0

02)

(0.0

03)

(1.7

56)

(0.0

05)

(0.0

03)

(0.0

03)

(0.0

05)

(0.0

04)

(0.0

02)

(0.0

06)

(0.0

02)

Obs

erva

tions

135,

786

102,

517

60,6

9429

3,02

123

3,07

823

3,07

815

2,34

515

1,82

031

6,11

627

7,33

122

1,75

7

R-s

quar

ed0.

293

0.14

50.

617

0.24

40.

234

0.23

90.

500

0.51

20.

040

0.26

70.

108

Not

e:T

hesa

mpl

ein

clud

esst

uden

tsin

grad

es3-

5w

hoat

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AU

SD.T

heun

itof

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rvat

ion

isa

stud

ent-

acad

emic

year

.Thi

sta

ble

repo

rts

the

effe

ctof

ast

anda

rdde

viat

ion

incr

ease

inth

eth

ree

mea

sure

sof

elem

enta

rysc

hool

teac

herv

alue

-add

ed(s

eeTa

ble

4)on

stud

ents

’hig

hsc

hool

outc

omes

.Spe

cific

ally

,eac

hco

lum

nof

the

tabl

ere

port

sth

eco

effic

ient

son

each

ofth

eth

ree

norm

aliz

edm

easu

res

ofte

ache

rva

lue-

adde

dfr

oman

OL

Sre

gres

sion

ofth

est

uden

ts’

high

scho

olou

tcom

eon

the

thre

em

easu

res

ofte

ache

rva

lue-

adde

dfo

rth

est

uden

ts’

teac

hers

ingr

ades

3,4,

or5,

alon

gw

ithth

eba

selin

eco

ntro

lsde

scri

bed

inse

ctio

n4.

1.T

heba

selin

eco

ntro

lsin

clud

ela

gsof

acu

bic

poly

nom

ialo

fthe

stud

ent’s

mat

hte

stsc

ore,

Eng

lish

test

scor

e,G

PA,a

ndef

fort

GPA

;acu

bic

poly

nom

ials

ofla

gged

clas

san

dgr

ade

leve

lmea

nsof

each

ofth

ose

vari

able

s;an

dcu

rren

tEng

lish

lear

ners

tatu

san

dla

gged

log

days

abse

nt,s

uspe

nsio

ns,a

ndhe

ldba

ckfo

rthe

indi

vidu

al,t

hecl

ass

aver

age,

and

the

grad

eav

erag

e.E

ach

ofth

ese

vari

able

sex

cept

Eng

lish

lear

ners

tatu

sis

fully

inte

ract

edw

ithgr

ade

fixed

effe

cts

and

aco

ntro

lfor

clas

ssi

zeis

incl

uded

.Sta

ndar

der

rors

clus

tere

dat

the

mod

alhi

ghsc

hool

leve

lare

repo

rted

inpa

rent

hese

s.**

*p

<0.

01, *

*p

<0.

05,a

nd*

p<

0.10

.

56

Tabl

e6:

Eff

ecto

fRep

laci

ngth

eB

otto

m5

Perc

ento

fEle

men

tary

Scho

olTe

ache

rson

Hig

hSc

hool

Out

com

es

Pool

edG

rade

s3-

5VA

LA

USD

Dro

pout

Took

SAT

SAT

Scor

eG

PAE

ffor

tGPA

Coo

pera

tion

GPA

Mat

h

CA

HSE

E

Eng

lish

CA

HSE

E

Day

s

Susp

ende

d

Log

Abs

ence

sH

eld

Bac

k

Pane

lA:U

sing

True

Val

ue-A

dded

Test

Scor

eVA

-0.0

04-0

.002

13.1

76**

*0.

008*

0.00

5*0.

009*

**0.

048*

**0.

035*

**-0

.000

-0.0

030.

001

(0.0

03)

(0.0

04)

(2.2

57)

(0.0

05)

(0.0

03)

(0.0

03)

(0.0

05)

(0.0

05)

(0.0

03)

(0.0

05)

(0.0

02)

Beh

avio

rVA

-0.0

07*

0.01

9***

5.44

60.

026*

**0.

013*

**0.

008*

0.02

8**

0.01

4-0

.007

**-0

.031

***

-0.0

11**

(0.0

04)

(0.0

06)

(4.5

31)

(0.0

08)

(0.0

05)

(0.0

04)

(0.0

11)

(0.0

09)

(0.0

03)

(0.0

10)

(0.0

05)

1 2(T

estS

core

+B

ehav

ior)

-0.0

08*

0.01

2**

12.2

80**

*0.

023*

**0.

012*

**0.

012*

**0.

050*

**0.

033*

**-0

.004

-0.0

22**

-0.0

07

(0.0

04)

(0.0

05)

(3.9

65)

(0.0

07)

(0.0

04)

(0.0

04)

(0.0

09)

(0.0

08)

(0.0

03)

(0.0

09)

(0.0

04)

Opt

imal

Thr

eeVA

-0.0

080.

019*

**13

.733

***

0.02

7***

0.01

4***

0.01

3***

0.05

3***

0.03

6***

-0.0

08-0

.031

***

-0.0

11*

Wei

ghte

dA

vg.

(0.0

06)

(0.0

07)

(3.1

44)

(0.0

09)

(0.0

05)

(0.0

04)

(0.0

08)

(0.0

07)

(0.0

05)

(0.0

11)

(0.0

05)

%G

ain

(Row

1to

Row

3)12

1%20

0%+

-7%

200%

129%

34%

4%-6

%20

0%+

200%

+20

0%+

Pane

lB:U

sing

Prev

ious

Thr

eeY

ears

ofSt

uden

tDat

a

Test

Scor

eVA

-0.0

02-0

.001

7.68

8***

0.00

4*0.

003*

0.00

5***

0.02

8***

0.02

0***

-0.0

00-0

.002

0.00

0

(0.0

02)

(0.0

02)

(1.3

17)

(0.0

03)

(0.0

02)

(0.0

01)

(0.0

03)

(0.0

03)

(0.0

02)

(0.0

03)

(0.0

01)

Beh

avio

rVA

-0.0

01*

0.00

3***

0.96

80.

005*

**0.

002*

**0.

001*

0.00

5**

0.00

3-0

.001

**-0

.006

***

-0.0

02**

(0.0

01)

(0.0

01)

(0.8

05)

(0.0

01)

(0.0

01)

(0.0

01)

(0.0

02)

(0.0

02)

(0.0

01)

(0.0

02)

(0.0

01)

1 2(T

estS

core

+B

ehav

ior)

-0.0

030.

001

6.46

3***

0.00

6**

0.00

4***

0.00

5***

0.02

4***

0.01

7***

-0.0

00-0

.004

-0.0

01

(0.0

02)

(0.0

02)

(1.3

71)

(0.0

03)

(0.0

02)

(0.0

01)

(0.0

03)

(0.0

03)

(0.0

01)

(0.0

03)

(0.0

02)

Opt

imal

Thr

eeVA

-0.0

030.

003*

**7.

688*

**0.

006*

*0.

004*

**0.

005*

**0.

028*

**0.

020*

**-0

.002

-0.0

06**

*-0

.002

**

Wei

ghte

dA

vg.

(0.0

02)

(0.0

01)

(1.3

17)

(0.0

03)

(0.0

02)

(0.0

01)

(0.0

03)

(0.0

03)

(0.0

01)

(0.0

02)

(0.0

01)

%G

ain

(Row

1to

Row

3)29

%20

0%+

-16%

36%

24%

-0%

-15%

-15%

142%

95%

200%

+

Not

e:T

hesa

mpl

ein

clud

esst

uden

tsin

grad

es3-

5w

hoat

tend

edhi

ghsc

hool

inth

eL

AU

SD.T

heun

itof

obse

rvat

ion

isa

stud

ent-

acad

emic

year

.Pan

elA

show

sth

eef

fect

ona

stud

ent’s

high

scho

olou

tcom

esof

bein

gas

sign

edan

aver

age

teac

heri

nste

adof

ate

ache

rin

the

botto

m5

perc

enta

sm

easu

red

byth

ete

ache

rval

ue-a

dded

vari

able

indi

cate

din

each

row

.The

sim

ulat

ion

uses

the

estim

ated

effe

cts

ofte

ache

rva

lue-

adde

dm

easu

res

onhi

ghsc

hool

outc

omes

(fro

mFi

gure

s2-

4an

dTa

ble

5),t

hew

ithin

-tea

cher

corr

elat

ions

betw

een

the

thre

ete

ache

rval

ued-

adde

dm

easu

res

(sho

wn

inTa

ble

4),a

ndas

sum

este

ache

rva

lue-

adde

dm

easu

res

are

norm

ally

dist

ribu

ted.

Row

1in

Pane

lAus

esa

mea

sure

ofte

ache

rs’

test

-sco

reva

lue-

adde

dto

repl

ace

the

botto

m5

perc

ento

fte

ache

rs.

The

refo

re,e

ach

cell

inro

w1

show

sth

eef

fect

ona

stud

ent’s

high

scho

olou

tcom

e(s

how

nin

the

colu

mn)

ifsh

ew

ere

tom

ove

from

ate

ache

rin

the

botto

m5

perc

ento

ftes

t-sc

ore

teac

herv

alue

-add

edto

anav

erag

ete

ache

r.R

ow2

ofPa

nelA

show

sth

eim

prov

emen

tin

outc

omes

for

am

ove

from

ate

ache

rin

the

botto

m5

perc

entt

oan

aver

age

teac

her

asm

easu

red

bybe

havi

orva

lue-

adde

d.R

ow3

ofPa

nelA

uses

the

aver

age

ofte

ache

rs’

test

-sco

reva

lue-

adde

dan

dth

eirb

ehav

iorv

alue

-add

ed.

Row

4of

Pane

lAus

esa

max

imiz

atio

npr

oced

ure

toch

oose

the

optim

alw

eigh

tsto

bepl

ace

onte

ache

rs’t

est-

scor

e,be

havi

or,a

ndle

arni

ng-s

kills

valu

e-ad

ded

tode

term

ine

the

botto

m5

perc

ento

fte

ache

rsfo

rth

ein

dica

ted

outc

ome

vari

able

.R

ow5

ofPa

nelA

show

sth

epe

rcen

tim

prov

emen

tin

stud

ents

’ou

tcom

esif

the

repl

acem

ento

fth

ebo

ttom

5pe

rcen

tof

teac

hers

ispe

rfor

med

byus

ing

the

aver

age

ofte

ache

rs’

test

-sco

rean

dbe

havi

orva

lue-

adde

din

stea

dof

just

teac

hers

’te

st-s

core

valu

e-ad

ded.

Pane

lBsh

ows

anal

ogou

sre

sults

usin

gte

ache

rs’

valu

e-ad

ded

base

don

only

the

thre

epr

evio

usye

ars

ofst

uden

tdat

a.T

hese

valu

ed-a

dded

mea

sure

sar

ees

timat

edus

ing

thre

epr

iory

ears

ofda

taon

each

teac

her,

alon

gw

ithth

eau

toco

rrel

atio

nsin

teac

hers

’val

ue-a

dded

acro

ssye

ars

show

nin

Figu

reA

.5.T

hest

anda

rder

rors

onth

ees

timat

edfo

reca

star

esh

own

inpa

rent

hese

s.**

*p

<0.

01, *

*p

<0.

05,a

nd*

p<

0.10

.

57

Tabl

e7:

Eff

ecto

fEle

men

tary

Scho

olTe

ache

rVal

ue-A

dded

onPr

edic

ted

Hig

hSc

hool

Out

com

es

Pool

edG

rade

s3-

5VA

LA

USD

Dro

pout

Took

SAT

SAT

Scor

eG

PAE

ffor

tGPA

Coo

pera

tion

GPA

Mat

h

CA

HSE

E

Eng

lish

CA

HSE

E

Day

s

Susp

ende

d

Log

Abs

ence

sH

eld

Bac

k

Test

Scor

eVA

0.00

00.

000

0.42

30.

000

0.00

00.

000

0.00

00.

000

0.00

00.

000

0.00

0

(0.0

00)

(0.0

01)

(0.9

82)

(0.0

01)

(0.0

01)

(0.0

01)

(0.0

02)

(0.0

02)

(0.0

00)

(0.0

01)

(0.0

00)

Beh

avio

rVA

0.00

1*-0

.001

*-2

.024

*-0

.003

**-0

.002

**-0

.001

**-0

.007

**-0

.006

***

0.00

0**

0.00

2*0.

001*

(0.0

00)

(0.0

01)

(1.0

30)

(0.0

01)

(0.0

01)

(0.0

01)

(0.0

03)

(0.0

02)

(0.0

00)

(0.0

01)

(0.0

00)

Lea

rnin

gSk

ills

VA0.

000

0.00

00.

229

0.00

00.

000

0.00

0-0

.001

0.00

10.

000

0.00

10.

000

(0.0

01)

(0.0

01)

(1.1

28)

(0.0

01)

(0.0

01)

(0.0

01)

(0.0

03)

(0.0

03)

(0.0

00)

(0.0

01)

(0.0

00)

Obs

erva

tions

66,3

5450

,137

29,2

6915

8,32

712

3,34

712

3,34

778

,651

78,4

3416

9,92

914

9,19

411

7,76

3

R-s

quar

ed0.

962

0.70

90.

750

0.68

10.

665

0.66

60.

702

0.75

00.

764

0.83

30.

737

Not

e:T

hesa

mpl

ein

clud

esst

uden

tsin

grad

es3-

5w

hoat

tend

edhi

ghsc

hool

inth

eL

AU

SD.T

heun

itof

obse

rvat

ion

isa

stud

ent-

acad

emic

year

.Thi

sta

ble

repo

rts

the

effe

ctof

ast

anda

rd-d

evia

tion

incr

ease

inth

eth

ree

mea

sure

sof

elem

enta

ryte

ache

rva

lue-

adde

d(s

eeTa

ble

4)on

stud

ents

’pr

edic

ted

high

scho

olou

tcom

esus

ing

doub

lela

gged

stud

ent

achi

evem

ent.

The

pred

icte

dou

tcom

esar

ecr

eate

dby

estim

atin

gan

OL

Sre

gres

sion

ofth

ehi

ghsc

hool

outc

ome

indi

cate

din

each

colu

mn

onth

ird-

orde

rpol

ynom

ials

ofdo

uble

lagg

ed(y

eart−

2)m

ath

and

Eng

lish

test

scor

es,G

PA,e

ffor

tGPA

,lea

rnin

gsk

ills

GPA

,log

abse

nces

,an

indi

cato

rfo

rsu

spen

sion

,an

indi

cato

rfo

rbe

ing

held

back

,and

anin

dica

tor

ofE

nglis

hle

arne

rst

atus

.T

heco

effic

ient

sob

tain

edfr

omth

isO

LS

regr

essi

onar

eth

enus

edto

pred

ict

stud

ents

’hig

hsc

hool

outc

omes

.Eac

hco

lum

nof

the

tabl

ere

port

sth

eco

effic

ient

son

each

ofth

eth

ree

norm

aliz

edm

easu

res

ofte

ache

rval

ue-a

dded

from

anO

LS

regr

essi

onof

the

stud

ents

’pre

dict

edhi

ghsc

hool

outc

ome

onth

eth

ree

mea

sure

sof

teac

herv

alue

-add

edfo

rthe

stud

ents

’tea

cher

sin

grad

es3,

4,or

5,al

ong

with

the

base

line

cont

rols

desc

ribe

din

sect

ion

4.1.

The

base

line

cont

rols

incl

ude

lags

ofa

cubi

cpo

lyno

mia

loft

hest

uden

t’sm

ath

test

scor

e,E

nglis

hte

stsc

ore,

GPA

,and

effo

rtG

PA;a

cubi

cpo

lyno

mia

lsof

lagg

edcl

ass

and

grad

ele

velm

eans

ofea

chof

thos

eva

riab

les;

and

curr

entE

nglis

hle

arne

rst

atus

and

lagg

edlo

gda

ysab

sent

,sus

pens

ions

,and

held

back

fort

hein

divi

dual

,the

clas

sav

erag

e,an

dth

egr

ade

aver

age.

Eac

hof

thes

eva

riab

les

exce

ptE

nglis

hle

arne

rsta

tus

isfu

llyin

tera

cted

with

grad

efix

edef

fect

san

da

cont

rolf

orcl

ass

size

isin

clud

ed.S

tand

ard

erro

rscl

uste

red

atth

em

odal

high

scho

olle

vela

rere

port

edin

pare

nthe

ses.

***

p<

0.01

, **

p<

0.05

,and

*p

<0.

10.

58

Tabl

e8:

Eff

ecto

fEle

men

tary

Scho

olTe

ache

rSub

ject

Em

phas

ison

Hig

hSc

hool

Out

com

es

Pool

edG

rade

s3-

5VA

LA

USD

Dro

pout

Took

SAT

SAT

Scor

eG

PAE

ffor

tGPA

Coo

pera

tion

GPA

Mat

h

CA

HSE

E

Eng

lish

CA

HSE

E

Day

s

Susp

ende

d

Log

Abs

ence

sH

eld

Bac

k

Mat

hG

PA0.

004

-0.0

065.

997

-0.0

05-0

.002

0.00

10.

021*

0.00

9-0

.000

0.00

10.

015*

**

(0.0

05)

(0.0

07)

(4.1

39)

(0.0

10)

(0.0

06)

(0.0

05)

(0.0

11)

(0.0

09)

(0.0

04)

(0.0

11)

(0.0

05)

Rea

ding

GPA

-0.0

11**

0.02

9***

9.83

0**

0.00

70.

006

0.00

50.

030*

*0.

028*

**0.

002

0.00

6-0

.005

(0.0

05)

(0.0

08)

(4.3

45)

(0.0

09)

(0.0

05)

(0.0

06)

(0.0

12)

(0.0

10)

(0.0

04)

(0.0

11)

(0.0

05)

Wri

ting

GPA

-0.0

01-0

.012

2.20

20.

016

0.00

80.

007

-0.0

030.

002

0.00

2-0

.005

-0.0

12**

(0.0

05)

(0.0

08)

(4.9

64)

(0.0

10)

(0.0

06)

(0.0

06)

(0.0

14)

(0.0

11)

(0.0

04)

(0.0

12)

(0.0

06)

Lis

teni

ngG

PA-0

.001

-0.0

104.

579

0.00

30.

002

0.00

40.

017

0.01

1-0

.003

-0.0

130.

001

(0.0

06)

(0.0

08)

(4.2

58)

(0.0

11)

(0.0

07)

(0.0

07)

(0.0

15)

(0.0

11)

(0.0

05)

(0.0

10)

(0.0

06)

Spea

king

GPA

0.01

4**

-0.0

03-1

1.32

3**

-0.0

25**

-0.0

13*

-0.0

14*

-0.0

28*

-0.0

25**

0.00

10.

009

0.00

1

(0.0

05)

(0.0

07)

(4.7

00)

(0.0

10)

(0.0

07)

(0.0

07)

(0.0

16)

(0.0

12)

(0.0

04)

(0.0

10)

(0.0

05)

His

tory

/Soc

ialS

cien

ceG

PA-0

.000

-0.0

031.

483

0.00

90.

008*

0.00

8*-0

.002

0.00

1-0

.001

0.00

6-0

.006

(0.0

06)

(0.0

06)

(3.8

24)

(0.0

08)

(0.0

05)

(0.0

05)

(0.0

11)

(0.0

07)

(0.0

03)

(0.0

12)

(0.0

06)

Scie

nce

GPA

-0.0

010.

000

-6.4

70-0

.000

-0.0

05-0

.009

-0.0

24*

-0.0

13-0

.003

-0.0

10-0

.003

(0.0

07)

(0.0

08)

(4.8

24)

(0.0

10)

(0.0

06)

(0.0

06)

(0.0

13)

(0.0

10)

(0.0

04)

(0.0

09)

(0.0

06)

Hea

lthE

duca

tion

GPA

0.00

30.

015*

*5.

572

0.00

70.

008

0.01

2**

0.02

3*0.

019*

-0.0

02-0

.000

0.00

2

(0.0

06)

(0.0

07)

(4.1

98)

(0.0

10)

(0.0

06)

(0.0

06)

(0.0

13)

(0.0

10)

(0.0

03)

(0.0

10)

(0.0

05)

PEG

PA-0

.007

0.01

1-0

.223

0.00

3-0

.005

-0.0

11*

-0.0

080.

000

-0.0

03-0

.012

-0.0

02

(0.0

06)

(0.0

07)

(4.7

95)

(0.0

09)

(0.0

05)

(0.0

06)

(0.0

11)

(0.0

09)

(0.0

04)

(0.0

09)

(0.0

04)

Art

sG

PA-0

.002

0.00

0-4

.331

-0.0

04-0

.005

-0.0

07-0

.009

-0.0

15*

0.00

30.

007

0.00

3

(0.0

06)

(0.0

07)

(5.1

69)

(0.0

10)

(0.0

06)

(0.0

06)

(0.0

12)

(0.0

08)

(0.0

03)

(0.0

11)

(0.0

05)

Obs

erva

tions

136,

125

102,

822

60,8

7529

3,56

923

3,52

923

3,52

915

2,69

315

2,16

231

6,74

027

7,85

822

2,17

4

R-s

quar

ed0.

293

0.14

60.

617

0.24

40.

234

0.24

00.

500

0.51

20.

039

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Tabl

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60

Appendix

Figure A.1: Secondary School Criteria for Marks

Note: The figure shows LAUSD’s secondary school teacher guidelines for giving achievement, effort, and cooperation grades.

61

Figure A.2: Share of Students Held Back by Grade0

.05

.1.1

5.2

.25

Sha

re H

eld

Bac

k

2 3 4 5 6 7 8 9 10 11 12Grade

Share Held Back by Grade

Note: The sample includes students in grades 3-5 who attended the LAUSD. The unit of observation is a student-academic year. The figure showsthe share of students held back by grade for all grades in the sample.

62

Figure A.3: Effect of Teacher Test-Score Value-Added Estimated using Year t +1 Test Scores onHigh School Outcomes

Note: The sample includes students in grades 3-5 who attended high school in the LAUSD. The unit of observation is a student-academic year.Figure A.3 shows binned scatter plots of residualized high school outcome variables and normalized teacher test-score value-added for grades 3, 4,or 5 estimated using year t +1 test-scores. We construct these plots by first residualizing the outcome and teacher value-added variables using thecontrols shown in equation (6). We then plot the mean values of both variables in 20 equally sized bins. Lastly, we add back the unconditional meanof both variables. We also plot the best linear fit estimated prior to binning the data, and report its slope coefficient and standard error, clustered atthe modal high school level. *** p < 0.01, ** p < 0.05, and * p < 0.10.

63

Figure A.4: Within Teacher Relationship in Value-Added Across Grades Taught-2

-1.5

-1-.

50

.51

1.5

2G

rade

4 T

est-

Sco

re V

alue

-Add

ed

-2 -1.5 -1 -.5 0 .5 1 1.5 2Grade 3 Test-Score Value-Added

Grade 3 and 4 Test-Score Value-Added

-2-1

.5-1

-.5

0.5

11.

52

Gra

de 4

Beh

avio

r V

alue

-Add

ed

-2 -1.5 -1 -.5 0 .5 1 1.5 2Grade 3 Behavior Value-Added

Grade 3 and 4 Behavior Value-Added

-2-1

.5-1

-.5

0.5

11.

52

Gra

de 4

Lea

rnin

g S

kills

Val

ue-A

dded

-2 -1.5 -1 -.5 0 .5 1 1.5 2Grade 3 Learning Skills Value-Added

Grade 3 and 4 Learning Skills Value-Added

Note: Unit of observation is a teacher-grade. The figure shows binned scatter plots of the within-teacher relationship in their average leave-one-outvalue-added in each grade taught.

64

Figure A.5: Autocorrelations of Teacher Value-Added Across Years

Note: Each graph shows the correlation between mean test-score residuals, using the baseline controls, across classes taught by the same teacher indifferent years. The first graph plots the autocorrections for elementary school and the second graph for secondary school.

65

Figure A.6: Effects of Teacher Value-Added on Student Attrition

Note: The unit of observation is a student-academic year. The figure shows plots of the effect of a teacher’s test-score, behavior, and learning-skillsvalue-added in grades 3, 4, or 5 on leads of student attrition (the first panel) and attrition in future grades (the second panel). In the first panel theplotted coefficients and standard errors (clustered at the modal high school level) are from a regression of the leads of attrition out of the sample ontest-score, behavior, and learning skills teacher value-added and the vector of controls specified in section 4.1, estimated separately for each lead.In the second panel the plotted coefficients and standard errors (clustered at the modal high school level) are from a regression of student attritionin a particular grade on test-score, behavior, and learning skills teacher value-added and the vector of controls specified in section 4.1, estimatedseparately for each grade.

66

Figure A.7: Effects of Test-Score and Behavior Teacher Value-Added by Grade Controlling forTracking

-.02

-.01

5-.

01-.

005

0.0

05C

oeffi

cien

t Est

imat

e

3 4 5 6 7 8 9 10 11 12Grade

Test Score Behavior

Test Score Sum = -0.007Behavior Sum = -0.083

HS Dropout

-.01

5-.

01-.

005

0.0

05.0

1C

oeffi

cien

t Est

imat

e

3 4 5 6 7 8 9 10 11 12Grade

Test Score Behavior

Test Score Sum = 0.016Behavior Sum = 0.025

HS Graduate

-.01

0.0

1.0

2.0

3C

oeffi

cien

t Est

imat

e

3 4 5 6 7 8 9 10 11 12Grade

Test Score Behavior

Test Score Sum = 0.078Behavior Sum = 0.077

Took SAT

-20

-10

010

20C

oeffi

cien

t Est

imat

e

3 4 5 6 7 8 9 10 11 12Grade

Test Score Behavior

Test Score Sum = 36Behavior Sum = -3

SAT Score-.

04-.

020

.02

.04

.06

Coe

ffici

ent E

stim

ate

3 4 5 6 7 8 9 10 11 12Grade

Test Score Behavior

Test Score Sum = -0.041Behavior Sum = 0.184

12th Grade GPA

-.02

0.0

2.0

4C

oeffi

cien

t Est

imat

e

3 4 5 6 7 8 9 10 11 12Grade

Test Score Behavior

Test Score Sum = -0.009Behavior Sum = 0.122

12th Grade Effort GPA

-.02

-.01

0.0

1.0

2.0

3C

oeffi

cien

t Est

imat

e

3 4 5 6 7 8 9 10 11 12Grade

Test Score Behavior

Test Score Sum = 0.006Behavior Sum = 0.079

12th Grade Cooperation GPA

-.05

0.0

5.1

Coe

ffici

ent E

stim

ate

3 4 5 6 7 8 9 10 11Grade

Test Score Behavior

Test Score Sum = 0.382Behavior Sum = 0.036

11th Grade Math Test Scores

-.02

0.0

2.0

4.0

6C

oeffi

cien

t Est

imat

e

3 4 5 6 7 8 9 10 11Grade

Test Score Behavior

Test Score Sum = 0.211Behavior Sum = -0.004

11th Grade English Test Scores

-.15

-.1

-.05

0.0

5C

oeffi

cien

t Est

imat

e

3 4 5 6 7 8 9 10 11 12Grade

Test Score Behavior

Test Score Sum = 0.000Behavior Sum = -0.411

12th Grade Log Days Absent

-.01

-.00

50

.005

Coe

ffici

ent E

stim

ate

3 4 5 6 7 8 9 10 11 12Grade

Test Score Behavior

Test Score Sum = 0.004Behavior Sum = -0.018

12th Grade Days Suspended

-.01

5-.

01-.

005

0.0

05C

oeffi

cien

t Est

imat

e

3 4 5 6 7 8 9 10 11Grade

Test Score Behavior

Test Score Sum = 0.001Behavior Sum = -0.032

11th Grade Held Back

Note: The sample includes students in grades 3-5 who attended high school in the LAUSD. The unit of observation is a student-academic year.The figure shows plots of the effect of test-score and behavior teacher value-added on high school outcomes by the grade level of the student. Theplotted coefficients and standard errors (clustered at the modal high school level) are from a regression of the high school outcome variable ontest-score and behavior teacher value-added, and the vector of controls for high school students specified in section 4.1, controls for future trackingfor elementary school students and current tracking for middle and high school students estimated separately for each grade. The middle and highschool teacher value-added scores are estimated controlling for tracking.

67

Figure A.8: Distribution of the Average Value-Added Score of Students’ Teachers from Grades4-11

0.2

.4.6

.81

Den

sity

-2 -1 0 1 2

Average Test-Score VA Average Behavior VA

Note: The sample is limited to students for whom we observe the value-added scores of their teachers for at least 7 of the 8 years they spend ingrades 4-11. The average value-added scores are computed within a student across their grade 4-11 teachers.

68

Tabl

eA

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ffec

tofE

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650.

0010

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0009

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edSE

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017)

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022)

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618)

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024)

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017)

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036)

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046)

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0040

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160

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057

Clu

ster

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025)

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033)

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391)

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051)

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026)

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050)

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027)

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060)

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051)

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028)

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058)

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031)

Lea

rnin

gSk

ills

VA

Poin

tEst

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e-0

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4-0

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0007

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021

0.00

170.

0010

Clu

ster

edSE

(0.0

024)

(0.0

027)

(1.7

562)

(0.0

052)

(0.0

032)

(0.0

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(0.0

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038)

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023)

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025)

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036)

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057)

(0.0

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033)

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057)

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045)

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029)

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068)

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029)

Not

e:T

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nof

the

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ere

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uden

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ades

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gw

ithth

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ntro

lsde

scri

bed

inse

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n4.

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ntro

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clud

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acu

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nom

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ath

test

scor

e,E

nglis

hte

stsc

ore,

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effo

rtG

PA;a

cubi

cpo

lyno

mia

lsof

lagg

edcl

ass

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ele

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eans

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chof

thos

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les;

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entE

nglis

hle

arne

rsta

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ions

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dual

,the

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sav

erag

e,an

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egr

ade

aver

age.

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hof

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eva

riab

les

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ptE

nglis

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arne

rsta

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llyin

tera

cted

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fect

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orcl

ass

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ard

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rscl

uste

red

atth

em

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ndbo

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ock

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69

Table A.2: Correlation of All Student Achievement Measures

MeasuresMath Test

Scores

English

Test ScoresGPA

Learning

Skills GPAEffort GPA

Log

Absences

Days

SuspendedHeld Back

Math Test Scores 1

English Test Scores 0.762 1

GPA 0.636 0.676 1

Learning Skills GPA 0.454 0.454 0.688 1

Effort GPA 0.527 0.539 0.828 0.774 1

Log Absences -0.186 -0.110 -0.170 -0.181 -0.176 1

Days Suspended -0.081 -0.076 -0.098 -0.184 -0.116 0.076 1

Held Back -0.072 -0.077 -0.106 -0.069 -0.082 0.018 0.010 1

Note: The sample includes students in grades 3-5 who attended high school in the LAUSD. The unit of observation is a student-academic year.Table A.2 reports the correlations between each measure of grades 3-5 student achievement.

70

Tabl

eA

.3:R

elat

ions

hip

betw

een

Ele

men

tary

Scho

olA

chie

vem

enta

ndH

igh

Scho

olO

utco

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10.

71

Table A.4: Correlation of All Elementary School Teacher Value-Added Measures

Grades 3-5 VAMath Test

Scores

English

Test ScoresGPA

Learning

Skills GPAEffort GPA

Log

Absences

Days

SuspendedHeld Back

Math Test Scores 1

English Test Scores 0.761 1

GPA 0.174 0.199 1

Learning Skills GPA 0.145 0.158 0.683 1

Effort GPA 0.142 0.154 0.767 0.760 1

Log Absences -0.064 -0.031 -0.122 -0.110 -0.111 1

Days Suspended -0.028 -0.033 -0.036 -0.078 -0.040 0.062 1

Held Back -0.023 -0.019 -0.016 -0.004 -0.011 0.010 0.006 1

Note: The sample includes students in grades 3-5 who attended high school in the LAUSD. The unit of observation is a student-academic year.Table 4 reports the correlations between measures of grades 3-5 teacher value-added. Each measure of teacher value-added is created as describedin section 4.1.

72

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73

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Eng

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lare

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rted

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rent

hese

s.**

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<0.

01, *

*p

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nd*

p<

0.10

.

74

Tabl

eA

.7:E

ffec

tofE

lem

enta

rySc

hool

Teac

herV

alue

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Hig

hSc

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Pool

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LA

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pout

Took

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Mat

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Day

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Susp

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Log

Abs

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Test

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-0.0

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001

-0.0

080.

000

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03)

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04)

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95)

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05)

(0.0

03)

(0.0

03)

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08)

(0.0

06)

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03)

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07)

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03)

Beh

avio

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0.00

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5.15

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.025

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07**

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03)

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04)

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44)

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06)

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04)

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04)

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06)

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05)

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03)

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07)

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31.

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04)

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04)

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09)

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05)

(0.0

05)

(0.0

07)

(0.0

06)

(0.0

04)

(0.0

08)

(0.0

04)

Obs

erva

tions

92,8

0370

,520

41,6

6120

9,36

416

5,85

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810

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5,79

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ed0.

362

0.14

10.

606

0.23

30.

223

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90.

487

0.49

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033

0.26

00.

101

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e:T

hesa

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clud

esst

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es3-

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and

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entE

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arne

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and

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rthe

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ach

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tere

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alhi

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lare

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rted

inpa

rent

hese

s.**

*p

<0.

01, *

*p

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nd*

p<

0.10

.

75

Tabl

eA

.8:E

ffec

tofE

lem

enta

rySc

hool

Teac

herV

alue

-Add

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hSc

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Out

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ctor

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Test

Scor

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ed

Gra

des

3-5

VATe

stSc

ore

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dex

Beh

avio

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x

Test

Scor

eVA

0.02

2***

0.00

6**

0.00

2

(0.0

03)

(0.0

03)

(0.0

02)

Obs

erva

tions

194,

271

296,

233

271,

186

R-s

quar

ed0.

577

0.26

30.

250

Beh

avio

rVal

ue-A

dded

Gra

des

3-5

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ore

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Beh

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0.01

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0.01

1**

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(0.0

05)

(0.0

04)

(0.0

04)

Obs

erva

tions

149,

637

233,

037

213,

435

R-s

quar

ed0.

563

0.25

20.

243

Lea

rnin

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ills

Val

ue-A

dded

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006

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10.

005

(0.0

04)

(0.0

06)

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05)

Obs

erva

tions

150,

085

233,

655

213,

992

R-s

quar

ed0.

563

0.25

20.

243

Not

e:T

hesa

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est

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hers

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3,4,

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gw

ithth

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ctio

n4.

1.T

heba

selin

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ela

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nom

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fthe

stud

ent’s

mat

hte

stsc

ore,

Eng

lish

test

scor

e,G

PA,a

ndef

fort

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;acu

bic

poly

nom

ials

ofla

gged

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able

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dcu

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tEng

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ners

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log

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loft

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each

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pect

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ndar

ere

port

edin

pare

nthe

ses.

76

Tabl

eA

.9:

Eff

ect

ofE

lem

enta

rySc

hool

Teac

her

Val

ue-A

dded

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igh

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Test

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.031

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]

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.865

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p−

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[0.0

00]

[0.0

60]

[0.8

97]

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Bon

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p−

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[0.0

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Lea

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p−

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.831

]

p−

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[0.5

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p−

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Not

e:T

hesa

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ialo

fthe

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ent’s

mat

hte

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ore,

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lish

test

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e,G

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fort

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;acu

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poly

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ials

ofla

gged

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n.

77

Tabl

eA

.10:

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p−

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.190

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.770

]

p−

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00]

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00]

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00]

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24]

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04]

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46]

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98]

[0.8

98]

[0.8

98]

[0.8

98]

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.000

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.000

]

p−

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.178

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.446

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][0

.141

][0

.002

][0

.141

][0

.002

][0

.028

]

p−

WY

[0.1

33]

[0.5

94]

[0.5

94]

[0.0

18]

[0.0

23]

[0.0

64]

[0.3

24]

[0.0

31]

[0.3

24]

[0.0

29]

[0.1

30]

p−

Bon

f[0

.078

][0

.729

][0

.729

][0

.009

][0

.014

][0

.046

][0

.356

][0

.017

][0

.356

][0

.013

][0

.106

]

p−

Sida

k[0

.076

][0

.596

][0

.596

][0

.009

][0

.014

][0

.046

][0

.324

][0

.017

][0

.324

][0

.013

][0

.102

]

Lea

rnin

gSk

ills

VAβ

-0.0

050.

001

-0.5

47-0

.002

-0.0

03-0

.003

0.00

0-0

.001

-0.0

020.

002

0.00

1

SE(0

.005

)(0

.004

)(1

.756

)(0

.005

)(0

.003

)(0

.003

)(0

.002

)(0

.003

)(0

.002

)(0

.006

)(0

.002

)

p−

valu

e[0

.279

][0

.854

][0

.757

][0

.682

][0

.334

][0

.287

][0

.949

][0

.611

][0

.368

][0

.770

][0

.697

]

p−

RW[0

.345

][0

.884

][0

.884

][0

.593

][0

.281

][0

.270

][0

.938

][0

.928

][0

.724

][0

.938

][0

.938

]

p−

WY

[0.5

49]

[0.9

32]

[0.9

32]

[0.6

87]

[0.4

42]

[0.4

42]

[0.9

71]

[0.9

71]

[0.8

84]

[0.9

71]

[0.9

71]

p−

Bon

f[0

.838

][1

.000

][1

.000

][0

.860

][0

.860

][0

.860

][1

.000

][1

.000

][1

.000

][1

.000

][1

.000

]

p−

Sida

k[0

.626

][0

.941

][0

.941

][0

.682

][0

.637

][0

.637

][0

.977

][0

.977

][0

.899

][0

.977

][0

.977

]

Not

e:T

hesa

mpl

ein

clud

esst

uden

tsin

grad

es3-

5w

hoat

tend

edhi

ghsc

hool

inth

eL

AU

SD.T

heun

itof

obse

rvat

ion

isa

stud

ent-

acad

emic

year

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sta

ble

repo

rts

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effe

ctof

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anda

rdde

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ion

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ease

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enta

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ed(s

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4)on

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ere

port

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ient

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each

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ree

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ache

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Sre

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ofth

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uden

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high

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eon

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res

ofte

ache

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adde

dfo

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uden

ts’

teac

hers

ingr

ades

3,4,

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gw

ithth

eba

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eco

ntro

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scri

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inse

ctio

n4.

1.T

heba

selin

eco

ntro

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clud

ela

gsof

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nom

ialo

fthe

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ent’s

mat

hte

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ore,

Eng

lish

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PA,a

ndef

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nom

ials

ofla

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lish

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ners

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age,

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ach

ofth

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cept

Eng

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fully

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ract

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ndar

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rors

clus

tere

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the

leve

loft

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ent

isex

pect

edto

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ndar

ere

port

edin

pare

nthe

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P-va

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are

repo

rted

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acke

tsth

atar

eca

lcul

ated

usin

gcl

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red

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dard

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oman

oW

olf(

2005

,201

6)co

rrec

tion,

the

Wes

tfall

You

ng(1

993)

algo

rith

m,t

heB

onfe

rron

icor

rect

ion,

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the

Sida

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olm

corr

ectio

n.T

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esar

eth

eth

ree

test

scor

es,t

heth

ree

GPA

mea

sure

s,an

dal

loth

erm

easu

res.

78

Tabl

eA

.11:

Eff

ect

ofE

lem

enta

rySc

hool

Teac

her

Val

ue-A

dded

onH

igh

Scho

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ultip

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gA

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mes

Pool

edG

rade

s3-

5VA

Out

put

Mat

h

CA

HSE

E

Eng

lish

CA

HSE

ESA

TSc

ore

GPA

Eff

ortG

PAC

oope

ratio

n

GPA

LA

USD

Dro

pout

Took

SAT

Day

s

Susp

ende

d

Log

Abs

ence

sH

eld

Bac

k

Test

Scor

eVA

β0.

022

0.01

66.

237

0.00

20.

003

0.00

5-0

.002

-0.0

020.

001

0.00

10.

001

SE(0

.003

)(0

.002

)(1

.262

)(0

.002

)(0

.001

)(0

.001

)(0

.002

)(0

.002

)(0

.001

)(0

.003

)(0

.001

)

p−

valu

e[0

.000

][0

.000

][0

.000

][0

.307

][0

.077

][0

.001

][0

.322

][0

.468

][0

.455

][0

.833

][0

.543

]

p−

RW[0

.000

][0

.000

][0

.000

][0

.646

][0

.135

][0

.000

][0

.646

][0

.770

][0

.770

][0

.776

][0

.770

]

p−

WY

[0.0

00]

[0.0

00]

[0.0

01]

[0.8

60]

[0.3

91]

[0.0

11]

[0.8

60]

[0.8

98]

[0.8

98]

[0.8

98]

[0.8

98]

p−

Bon

f[0

.000

][0

.000

][0

.000

][1

.000

][0

.540

][0

.005

][1

.000

][1

.000

][1

.000

][1

.000

][1

.000

]

p−

Sida

k[0

.000

][0

.000

][0

.000

][0

.890

][0

.430

][0

.005

][0

.890

][0

.912

][0

.912

][0

.912

][0

.912

]

Beh

avio

rVA

β0.

013

0.00

41.

955

0.01

30.

007

0.00

5-0

.003

0.01

0-0

.003

-0.0

16-0

.006

SE(0

.006

)(0

.005

)(2

.139

)(0

.004

)(0

.003

)(0

.002

)(0

.002

)(0

.003

)(0

.002

)(0

.005

)(0

.003

)

p−

valu

e[0

.026

][0

.390

][0

.365

][0

.003

][0

.007

][0

.046

][0

.188

][0

.004

][0

.178

][0

.003

][0

.035

]

p−

RW[0

.061

][0

.446

][0

.446

][0

.010

][0

.020

][0

.083

][0

.318

][0

.012

][0

.318

][0

.010

][0

.074

]

p−

WY

[0.2

17]

[0.5

94]

[0.5

94]

[0.0

66]

[0.0

98]

[0.2

68]

[0.5

66]

[0.0

78]

[0.5

66]

[0.0

66]

[0.2

51]

p−

Bon

f[0

.182

][0

.729

][0

.729

][0

.030

][0

.058

][0

.230

][0

.711

][0

.038

][0

.711

][0

.030

][0

.212

]

p−

Sida

k[0

.169

][0

.596

][0

.596

][0

.029

][0

.056

][0

.210

][0

.543

][0

.038

][0

.543

][0

.029

][0

.194

]

Lea

rnin

gSk

ills

VAβ

-0.0

050.

001

-0.5

47-0

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-0.0

03-0

.003

0.00

0-0

.001

-0.0

020.

002

0.00

1

SE(0

.005

)(0

.004

)(1

.756

)(0

.005

)(0

.003

)(0

.003

)(0

.002

)(0

.003

)(0

.002

)(0

.006

)(0

.002

)

p−

valu

e[0

.279

][0

.854

][0

.757

][0

.682

][0

.334

][0

.287

][0

.949

][0

.611

][0

.368

][0

.770

][0

.697

]

p−

RW[0

.705

][0

.987

][0

.987

][0

.987

][0

.782

][0

.706

][0

.987

][0

.979

][0

.827

][0

.987

][0

.987

]

p−

WY

[0.8

95]

[0.9

96]

[0.9

96]

[0.9

96]

[0.9

32]

[0.8

95]

[0.9

96]

[0.9

95]

[0.9

48]

[0.9

96]

[0.9

96]

p−

Bon

f[1

.000

][1

.000

][1

.000

][1

.000

][1

.000

][1

.000

][1

.000

][1

.000

][1

.000

][1

.000

][1

.000

]

p−

Sida

k[0

.973

][0

.999

][0

.999

][0

.999

][0

.974

][0

.973

][0

.999

][0

.999

][0

.974

][0

.999

][0

.999

]

Not

e:T

hesa

mpl

ein

clud

esst

uden

tsin

grad

es3-

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eL

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repo

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ctof

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Sre

gres

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uden

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ingr

ades

3,4,

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ithth

eba

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eco

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scri

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inse

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1.T

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eco

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ialo

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mat

hte

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ore,

Eng

lish

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fort

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ials

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cept

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fully

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ract

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loft

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2005

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rith

m,t

heB

onfe

rron

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rect

ion,

and

the

Sida

k-H

olm

corr

ectio

n.

79

Tabl

eA

.12:

Eff

ecto

fAll

Ele

men

tary

Scho

olTe

ache

rVal

ue-A

dded

Mea

sure

son

Hig

hSc

hool

Out

com

esfr

omSe

para

teR

egre

ssio

ns

Pool

edG

rade

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LA

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Dro

pout

Took

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Scor

eG

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ffor

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pera

tion

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Mat

h

CA

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lish

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Day

s

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Log

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Mat

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02)

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02)

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Test

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03)

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05)

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10)

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07)

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02)

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5L

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Skill

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04)

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14)

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12)

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09)

(0.0

08)

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18)

(0.0

17)

(0.0

06)

(0.0

15)

(0.0

12)

Not

e:T

hesa

mpl

ein

clud

esst

uden

tsin

grad

es3-

5w

hoat

tend

edhi

ghsc

hool

inth

eL

AU

SD.T

heun

itof

obse

rvat

ion

isa

stud

ent-

acad

emic

year

.Thi

sta

ble

repo

rts

the

effe

ctof

ast

anda

rdde

viat

ion

incr

ease

inth

em

easu

res

ofel

emen

tary

scho

olte

ache

rva

lue-

adde

d(s

eeTa

ble

A.4

)on

stud

ents

’hi

ghsc

hool

outc

omes

.Sp

ecifi

cally

,eac

hco

lum

nof

the

tabl

ere

port

sth

eco

effic

ient

son

each

ofth

eno

rmal

ized

mea

sure

sof

teac

herv

alue

-add

edfr

oman

OL

Sre

gres

sion

ofth

est

uden

ts’h

igh

scho

olou

tcom

eon

each

ofth

em

easu

res

ofte

ache

rval

ue-a

dded

fort

hest

uden

ts’t

each

ers

ingr

ades

3,4,

or5,

alon

gw

ithth

eba

selin

eco

ntro

lsde

scri

bed

inse

ctio

n4.

1.T

heba

selin

eco

ntro

lsin

clud

ela

gsof

acu

bic

poly

nom

ialo

fthe

stud

ent’s

mat

hte

stsc

ore,

Eng

lish

test

scor

e,G

PA,a

ndef

fort

GPA

;acu

bic

poly

nom

ials

ofla

gged

clas

san

dgr

ade

leve

lmea

nsof

each

ofth

ose

vari

able

s;an

dcu

rren

tEng

lish

lear

ners

tatu

san

dla

gged

log

days

abse

nt,s

uspe

nsio

ns,a

ndhe

ldba

ckfo

rthe

indi

vidu

al,t

hecl

ass

aver

age,

and

the

grad

eav

erag

e.E

ach

ofth

ese

vari

able

sex

cept

Eng

lish

lear

ners

tatu

sis

fully

inte

ract

edw

ithgr

ade

fixed

effe

cts

and

aco

ntro

lfor

clas

ssi

zeis

incl

uded

.Sta

ndar

der

rors

clus

tere

dat

the

mod

alhi

ghsc

hool

leve

lare

repo

rted

inpa

rent

hese

s.**

*p

<0.

01, *

*p

<0.

05,a

nd*

p<

0.10

.

80

Tabl

eA

.13:

Eff

ecto

fAll

Ele

men

tary

Scho

olTe

ache

rVal

ue-A

dded

Mea

sure

son

Hig

hSc

hool

Out

com

es

Gra

des

3-5

VAL

AU

SD

Dro

pout

Took

SAT

SAT

Scor

eG

PAE

ffor

tGPA

Coo

pera

tion

GPA

Mat

h

CA

HSE

E

Eng

lish

CA

HSE

E

Day

s

Susp

ende

d

Log

Abs

ence

sH

eld

Bac

k

Mat

hTe

stSc

ores

VA0.

003

0.00

3-8

.234

***

-0.0

14**

*-0

.008

***

-0.0

06**

-0.0

00-0

.018

***

0.00

20.

011*

*0.

005

(0.0

04)

(0.0

04)

(2.2

90)

(0.0

05)

(0.0

03)

(0.0

03)

(0.0

06)

(0.0

05)

(0.0

03)

(0.0

05)

(0.0

03)

Eng

lish

Test

Scor

esVA

-0.0

06-0

.007

18.2

00**

*0.

019*

**0.

013*

**0.

013*

**0.

033*

**0.

043*

**-0

.001

-0.0

12-0

.004

(0.0

04)

(0.0

04)

(2.8

37)

(0.0

05)

(0.0

03)

(0.0

03)

(0.0

07)

(0.0

06)

(0.0

03)

(0.0

07)

(0.0

04)

GPA

VA0.

001

0.02

4***

7.71

60.

003

0.00

10.

002

0.00

80.

016*

0.00

2-0

.002

-0.0

11**

(0.0

05)

(0.0

07)

(5.4

23)

(0.0

09)

(0.0

06)

(0.0

06)

(0.0

11)

(0.0

09)

(0.0

04)

(0.0

12)

(0.0

05)

Lea

rnin

gSk

ills

GPA

VA-0

.007

-0.0

18**

*-5

.377

-0.0

12-0

.007

-0.0

03-0

.018

*-0

.011

0.00

0-0

.011

0.00

8*

(0.0

06)

(0.0

06)

(4.8

13)

(0.0

08)

(0.0

05)

(0.0

05)

(0.0

10)

(0.0

09)

(0.0

03)

(0.0

11)

(0.0

04)

Eff

ortG

PAVA

0.00

30.

002

-1.6

020.

014*

0.00

5-0

.003

0.00

90.

001

-0.0

070.

009

-0.0

01

(0.0

05)

(0.0

08)

(5.8

99)

(0.0

08)

(0.0

05)

(0.0

05)

(0.0

12)

(0.0

10)

(0.0

05)

(0.0

10)

(0.0

05)

Log

Abs

ence

sVA

0.00

50.

000

-10.

348*

*-0

.024

**-0

.020

**-0

.017

**-0

.027

*-0

.010

0.00

10.

011

0.01

4*

(0.0

08)

(0.0

10)

(4.4

00)

(0.0

10)

(0.0

08)

(0.0

08)

(0.0

15)

(0.0

12)

(0.0

05)

(0.0

10)

(0.0

08)

Day

sSu

spen

ded

VA0.

011

-0.0

21**

1.99

0-0

.017

-0.0

07-0

.002

-0.0

26*

-0.0

010.

008*

*0.

053*

**-0

.002

(0.0

07)

(0.0

09)

(6.3

49)

(0.0

11)

(0.0

06)

(0.0

05)

(0.0

15)

(0.0

12)

(0.0

04)

(0.0

13)

(0.0

05)

Hel

dB

ack

VA0.

002

-0.0

0513

.027

*-0

.035

***

-0.0

09-0

.004

0.01

30.

021

0.00

5-0

.001

0.00

9

(0.0

13)

(0.0

14)

(7.2

67)

(0.0

12)

(0.0

08)

(0.0

07)

(0.0

17)

(0.0

17)

(0.0

05)

(0.0

15)

(0.0

12)

Obs

erva

tions

135,

786

102,

517

60,6

9429

3,02

123

3,07

823

3,07

815

2,34

515

1,82

031

6,11

627

7,33

122

1,75

7

R-s

quar

ed0.

293

0.14

50.

617

0.24

40.

234

0.23

90.

500

0.51

30.

040

0.26

70.

108

Not

e:T

his

tabl

ere

port

sth

eef

fect

ofa

stan

dard

devi

atio

nin

crea

sein

the

mea

sure

sof

elem

enta

rysc

hool

teac

herv

alue

-add

ed(s

eeTa

ble

A.4

)on

stud

ents

’hig

hsc

hool

outc

omes

.Spe

cific

ally

,eac

hco

lum

nof

the

tabl

ere

port

sth

eco

effic

ient

son

each

ofth

eno

rmal

ized

mea

sure

sof

teac

herv

alue

-add

edfr

oman

OL

Sre

gres

sion

ofth

est

uden

ts’h

igh

scho

olou

tcom

eon

the

mea

sure

sof

teac

herv

alue

-add

edfo

rthe

stud

ents

’tea

cher

sin

grad

es3,

4,or

5,al

ong

with

the

base

line

cont

rols

desc

ribe

din

sect

ion

4.1.

The

base

line

cont

rols

incl

ude

lags

ofa

cubi

cpo

lyno

mia

loft

hest

uden

t’sm

ath

test

scor

e,E

nglis

hte

stsc

ore,

GPA

,and

effo

rtG

PA;a

cubi

cpo

lyno

mia

lsof

lagg

edcl

ass

and

grad

ele

velm

eans

ofea

chof

thos

eva

riab

les;

and

curr

entE

nglis

hle

arne

rst

atus

and

lagg

edlo

gda

ysab

sent

,sus

pens

ions

,and

held

back

for

the

indi

vidu

al,t

hecl

ass

aver

age,

and

the

grad

eav

erag

e.E

ach

ofth

ese

vari

able

sex

cept

Eng

lish

lear

ner

stat

usis

fully

inte

ract

edw

ithgr

ade

fixed

effe

cts

and

aco

ntro

lfor

clas

ssi

zeis

incl

uded

.St

anda

rder

rors

clus

tere

dat

the

mod

alhi

ghsc

hool

leve

lare

repo

rted

inpa

rent

hese

s.**

*p

<0.

01, *

*p

<0.

05,a

nd*

p<

0.10

.

81

Tabl

eA

.14:

Eff

ecto

fEle

men

tary

Scho

olTe

ache

rVal

ue-A

dded

onH

igh

Scho

olO

utco

mes

Usi

ngA

ltern

ativ

eG

roup

ing

Pool

edG

rade

s3-

5VA

LA

USD

Dro

pout

Took

SAT

SAT

Scor

eG

PAE

ffor

tGPA

Coo

pera

tion

GPA

Mat

h

CA

HSE

E

Eng

lish

CA

HSE

E

Day

s

Susp

ende

d

Log

Abs

ence

sH

eld

Bac

k

Test

Scor

eVA

-0.0

02-0

.002

6.23

8***

0.00

30.

003*

0.00

5***

0.02

2***

0.01

6***

0.00

10.

000

0.00

1

(0.0

02)

(0.0

02)

(1.2

65)

(0.0

02)

(0.0

01)

(0.0

01)

(0.0

03)

(0.0

03)

(0.0

01)

(0.0

03)

(0.0

01)

Beh

avio

rVA

-0.0

030.

005*

0.61

90.

011*

**0.

006*

**0.

004*

*0.

010*

*0.

000

-0.0

02-0

.015

***

-0.0

03

(0.0

02)

(0.0

03)

(1.5

01)

(0.0

03)

(0.0

02)

(0.0

02)

(0.0

04)

(0.0

04)

(0.0

02)

(0.0

04)

(0.0

02)

All

GPA

VA-0

.001

0.00

4*0.

849

0.00

3-0

.000

-0.0

020.

000

0.00

4-0

.003

-0.0

04-0

.002

(0.0

02)

(0.0

02)

(2.0

44)

(0.0

05)

(0.0

03)

(0.0

03)

(0.0

05)

(0.0

04)

(0.0

02)

(0.0

06)

(0.0

02)

Obs

erva

tions

135,

786

102,

517

60,6

9429

3,02

123

3,07

823

3,07

815

2,34

515

1,82

031

6,11

627

7,33

122

1,75

7

R-s

quar

ed0.

293

0.14

50.

617

0.24

40.

234

0.23

90.

500

0.51

20.

040

0.26

70.

108

Not

e:T

hesa

mpl

ein

clud

esst

uden

tsin

grad

es3-

5w

hoat

tend

edhi

ghsc

hool

inth

eL

AU

SD.T

heun

itof

obse

rvat

ion

isa

stud

ent-

acad

emic

year

.Thi

sta

ble

repo

rts

the

effe

ctof

ast

anda

rdde

viat

ion

incr

ease

inth

eth

ree

alte

rnat

ive

mea

sure

sof

elem

enta

rysc

hool

teac

herv

alue

-add

edon

stud

ents

’hig

hsc

hool

outc

omes

.The

test

-sco

reva

lue-

adde

dis

com

pute

dus

ing

the

teac

hers

’val

ue-a

dded

form

ath

and

Eng

lish

test

scor

es.

The

beha

vior

valu

e-ad

ded

isco

mpu

ted

usin

gte

ache

rs’

valu

e-ad

ded

for

susp

ensi

ons,

log

days

abse

nt,a

ndno

tpro

gres

sing

toth

ene

xtgr

ade

ontim

e(h

eld

back

).T

heA

llG

PAva

lue-

adde

dis

com

pute

dus

ing

teac

hers

’val

ue-a

dded

forG

PA,e

ffor

tGPA

,wor

kan

dst

udy

habi

tGPA

,and

lear

ning

and

soci

alsk

ills

GPA

.Eac

hco

lum

nof

the

tabl

ere

port

sth

eco

effic

ient

son

each

ofth

eth

ree

alte

rnat

ive

mea

sure

sof

teac

herv

alue

-add

edfr

oman

OL

Sre

gres

sion

ofth

est

uden

ts’h

igh

scho

olou

tcom

eon

the

thre

eal

tern

ativ

em

easu

res

ofte

ache

rval

ue-a

dded

fort

hest

uden

ts’t

each

ers

ingr

ades

3,4,

or5

alon

gw

ithth

eba

selin

eco

ntro

lsde

scri

bed

inse

ctio

n4.

1.T

heba

selin

eco

ntro

lsin

clud

ela

gsof

acu

bic

poly

nom

ialo

fthe

stud

ent’s

mat

hte

stsc

ore,

Eng

lish

test

scor

e,G

PA,a

ndef

fort

GPA

;acu

bic

poly

nom

ials

ofla

gged

clas

san

dgr

ade

leve

lmea

nsof

each

ofth

ose

vari

able

s;an

dcu

rren

tEng

lish

lear

ners

tatu

san

dla

gged

log

days

abse

nt,s

uspe

nsio

ns,a

ndhe

ldba

ckfo

rthe

indi

vidu

al,t

hecl

ass

aver

age,

and

the

grad

eav

erag

e.E

ach

ofth

ese

vari

able

sex

cept

Eng

lish

lear

ners

tatu

sis

fully

inte

ract

edw

ithgr

ade

fixed

effe

cts

and

aco

ntro

lfor

clas

ssi

zeis

incl

uded

.Sta

ndar

der

rors

clus

tere

dat

the

mod

alhi

ghsc

hool

leve

lare

repo

rted

inpa

rent

hese

s.**

*p

<0.

01, *

*p

<0.

05,a

nd*

p<

0.10

.

82

Tabl

eA

.15:

Est

imat

esof

Fore

cast

Bia

s

Pool

edG

rade

s3-

5VA

Mat

hTe

st

Scor

e

EL

ATe

st

Scor

eG

PAH

abit

GPA

Eff

ortG

PAL

og

Abs

ence

sSu

spen

ded

Hel

dba

ck

Pane

lA:U

sing

Act

ualR

esid

ualiz

edC

orre

spon

ding

Out

com

eV

aria

ble

Cor

resp

ondi

ngTe

ache

r0.

989*

*0.

987

1.02

70.

997

1.02

01.

057

1.09

01.

195

Val

ue-A

dded

Mea

sure

(0.0

05)

(0.0

10)

(0.0

32)

(0.0

30)

(0.0

30)

(0.0

92)

(0.1

88)

(0.3

65)

Obs

erva

tions

668,

480

668,

712

407,

248

402,

882

402,

882

468,

446

476,

790

369,

890

Pane

lB:U

sing

Pred

icte

dC

orre

spon

ding

Out

com

eV

aria

ble

Cor

resp

ondi

ngTe

ache

r0.

004

0.00

10.

013*

-0.0

010.

004

-0.0

160.

007

-0.0

01

Val

ue-A

dded

Mea

sure

(0.0

03)

(0.0

05)

(0.0

07)

(0.0

05)

(0.0

04)

(0.0

23)

(0.0

11)

(0.0

03)

Obs

erva

tions

413,

826

413,

826

323,

910

323,

568

323,

568

344,

370

345,

914

322,

806

Not

e:T

hesa

mpl

ein

clud

esst

uden

tsin

grad

es3-

5w

hoat

tend

edhi

ghsc

hool

inth

eL

AU

SD.T

heun

itof

obse

rvat

ion

isa

stud

ent-

acad

emic

year

.Eac

hce

llin

Pane

lAre

port

sth

eco

effic

ient

onth

ein

dica

ted

elem

enta

rysc

hool

teac

herv

alue

-add

edm

easu

refr

oman

OL

Sre

gres

sion

ofst

uden

ts’r

esid

ualiz

ed(u

sing

the

base

line

cont

rols

)tes

tsco

re,G

PA,o

rbeh

avio

ralm

easu

reon

the

teac

herv

alue

-add

edm

easu

reca

lcul

ated

usin

gth

atou

tcom

e.Te

ache

rval

ued-

adde

dis

estim

ated

usin

gth

ele

ave-

outy

eara

ppro

ach

deta

iled

inse

ctio

n4.

1,an

dsc

aled

sobo

thth

eou

tcom

ean

dva

lue-

adde

dm

easu

rear

ein

the

sam

eun

its.

Eac

hce

llin

Pane

lBre

port

sth

eco

effic

ient

onth

ein

dica

ted

teac

her

valu

e-ad

ded

mea

sure

from

anO

LS

regr

essi

onof

stud

ents

’pr

edic

ted

outc

ome

(usi

ngdo

uble

-lag

ged

stud

enta

chie

vem

ent)

onte

ache

rs’

valu

e-ad

ded

mea

sure

afte

ral

lva

riab

les

are

resi

dual

ized

usin

gth

eba

selin

eco

ntro

ls.

The

pred

icte

dou

tcom

esar

ecr

eate

dby

estim

atin

gan

OL

Sre

gres

sion

ofth

eou

tcom

ein

dica

ted

inea

chco

lum

non

thir

d-or

der

poly

nom

ials

ofdo

uble

lagg

ed(y

eart−

2)m

ath

and

Eng

lish

test

scor

es,G

PA,e

ffor

tGPA

,lea

rnin

g-sk

ills

GPA

,log

abse

nces

,an

indi

cato

rfo

rsu

spen

sion

,an

indi

cato

rfo

rbe

ing

held

back

,and

anin

dica

toro

fEng

lish

lear

ners

tatu

s.T

heco

effic

ient

sob

tain

edfr

omth

isO

LS

regr

essi

onar

eth

enus

edto

pred

icts

tude

nts’

outc

omes

.Sta

ndar

der

rors

clus

tere

dat

the

mod

alhi

ghsc

hool

leve

lare

repo

rted

inpa

rent

hese

s.

83

Tabl

eA

.16:

Eff

ecto

fEle

men

tary

Scho

olTe

ache

rVal

ue-A

dded

onH

igh

Scho

olO

utco

mes

Usi

ngSc

hool

-Gra

deV

aria

tion

Pool

edG

rade

s3-

5VA

LA

USD

Dro

pout

Took

SAT

SAT

Scor

eG

PAE

ffor

tGPA

Coo

pera

tion

GPA

Mat

h

CA

HSE

E

Eng

lish

CA

HSE

E

Day

s

Susp

ende

d

Log

Abs

ence

sH

eld

Bac

k

Test

Scor

eVA

-0.0

15-0

.008

3.08

2-0

.001

-0.0

040.

000

0.03

3**

0.01

8-0

.004

-0.0

04-0

.002

(0.0

13)

(0.0

09)

(5.8

44)

(0.0

10)

(0.0

06)

(0.0

05)

(0.0

16)

(0.0

15)

(0.0

06)

(0.0

11)

(0.0

06)

Beh

avio

rVA

-0.0

390.

029

2.10

10.

043*

*0.

026*

*0.

021*

*0.

053

0.03

6-0

.009

-0.0

30-0

.013

(0.0

27)

(0.0

18)

(12.

527)

(0.0

20)

(0.0

12)

(0.0

10)

(0.0

34)

(0.0

31)

(0.0

12)

(0.0

23)

(0.0

12)

Lea

rnin

gSk

ills

VA0.

003

-0.0

22-4

.208

-0.0

49**

*-0

.020

**-0

.018

**-0

.047

*-0

.039

0.01

7*0.

024

0.00

8

(0.0

24)

(0.0

16)

(9.6

83)

(0.0

16)

(0.0

09)

(0.0

08)

(0.0

27)

(0.0

26)

(0.0

10)

(0.0

19)

(0.0

10)

Obs

erva

tions

3,10

63,

094

2,87

86,

250

5,17

55,

175

4,04

44,

043

6,29

06,

233

5,14

1

R-s

quar

ed0.

352

0.02

10.

001

0.00

60.

006

0.00

40.

021

0.01

00.

016

0.32

40.

037

Not

e:T

hesa

mpl

ein

clud

esst

uden

tsin

grad

es3-

5w

hoat

tend

edhi

ghsc

hool

inth

eL

AU

SD.T

heun

itof

obse

rvat

ion

isa

scho

ol-g

rade

-yea

r.T

his

tabl

ere

port

sth

eef

fect

ofa

stan

dard

devi

atio

nin

crea

sein

the

thre

em

easu

res

ofel

emen

tary

scho

olte

ache

rva

lue-

adde

d(s

eeTa

ble

4)on

stud

ents

’hi

ghsc

hool

outc

omes

usin

gsc

hool

-gra

deva

riat

ion

inte

ache

rs’

valu

e-ad

ded.

Spec

ifica

lly,e

ach

colu

mn

ofth

eta

ble

repo

rts

the

coef

ficie

nts

onth

efir

stdi

ffer

ence

inth

esc

hool

-gra

dele

velm

ean

ofea

chof

the

thre

em

easu

res

ofte

ache

rval

ue-a

dded

from

ast

uden

twei

ghte

dO

LS

regr

essi

onof

the

first

diff

eren

cein

the

scho

ol-g

rade

leve

lmea

nof

the

outc

ome

vari

able

onth

efir

stdi

ffer

ence

inth

esc

hool

-gra

dele

velm

ean

ofea

chof

the

thre

em

easu

res

ofte

ache

rva

lue-

adde

dfo

rgr

ades

3-5

,alo

ngw

ithye

arfix

edef

fect

s.St

anda

rder

rors

clus

tere

dat

the

mod

alhi

ghsc

hool

leve

lare

repo

rted

inpa

rent

hese

s.**

*p

<0.

01, *

*p

<0.

05,a

nd*

p<

0.10

.

84

Tabl

eA

.17:

Eff

ecto

fEle

men

tary

Scho

olTe

ache

rVal

ue-A

dded

onH

igh

Scho

olO

utco

mes

-Con

trol

ling

forF

utur

eTr

acki

ng

Pool

edG

rade

s3-

5VA

LA

USD

Dro

pout

Took

SAT

SAT

Scor

eG

PAE

ffor

tGPA

Coo

pera

tion

GPA

Mat

h

CA

HSE

E

Eng

lish

CA

HSE

E

Day

s

Susp

ende

d

Log

Abs

ence

sH

eld

Bac

k

Test

Scor

eVA

-0.0

03*

-0.0

014.

924*

**-0

.001

0.00

10.

003*

**0.

020*

**0.

015*

**0.

001

0.00

40.

001

(0.0

02)

(0.0

02)

(1.0

52)

(0.0

02)

(0.0

01)

(0.0

01)

(0.0

02)

(0.0

02)

(0.0

01)

(0.0

03)

(0.0

01)

Beh

avio

rVA

-0.0

010.

008*

**1.

765

0.01

0***

0.00

5***

0.00

4*0.

010*

*0.

001

-0.0

02-0

.014

***

-0.0

05**

(0.0

02)

(0.0

03)

(1.2

08)

(0.0

03)

(0.0

02)

(0.0

02)

(0.0

04)

(0.0

03)

(0.0

02)

(0.0

04)

(0.0

02)

Lea

rnin

gSk

ills

VA-0

.000

-0.0

03-1

.498

-0.0

04-0

.003

-0.0

04-0

.008

*-0

.000

-0.0

020.

002

0.00

0

(0.0

02)

(0.0

02)

(1.4

86)

(0.0

04)

(0.0

02)

(0.0

03)

(0.0

04)

(0.0

03)

(0.0

02)

(0.0

05)

(0.0

02)

Obs

erva

tions

135,

786

102,

517

60,6

9429

3,02

123

3,07

823

3,07

815

2,34

515

1,82

031

6,11

627

7,33

122

1,75

7

R-s

quar

ed0.

401

0.29

80.

708

0.39

20.

418

0.40

60.

609

0.57

80.

051

0.30

90.

201

Not

e:T

his

tabl

ere

port

sth

eef

fect

ofa

stan

dard

devi

atio

nin

crea

sein

the

thre

em

easu

res

ofel

emen

tary

scho

olte

ache

rva

lue-

adde

d(s

eeTa

ble

4)on

stud

ents

’hi

ghsc

hool

outc

omes

.Sp

ecifi

cally

,eac

hco

lum

nof

the

tabl

ere

port

sth

eco

effic

ient

son

each

ofth

eth

ree

norm

aliz

edm

easu

res

ofte

ache

rval

ue-a

dded

from

anO

LS

regr

essi

onof

the

stud

ents

’hig

hsc

hool

outc

ome

onth

eth

ree

mea

sure

sof

teac

her

valu

e-ad

ded

fort

hest

uden

ts’t

each

ers

ingr

ades

3,4,

or5,

alon

gw

ithth

eba

selin

eco

ntro

lsde

scri

bed

inse

ctio

n4.

1.T

heba

selin

eco

ntro

lsin

clud

ela

gsof

acu

bic

poly

nom

ialo

fthe

stud

ent’s

mat

hte

stsc

ore,

Eng

lish

test

scor

e,G

PA,a

ndef

fort

GPA

;acu

bic

poly

nom

ials

ofla

gged

clas

san

dgr

ade

leve

lmea

nsof

each

ofth

ose

vari

able

s;an

dcu

rren

tEng

lish

lear

ners

tatu

san

dla

gged

log

days

abse

nt,s

uspe

nsio

ns,

and

held

back

for

the

indi

vidu

al,t

hecl

ass

aver

age,

and

the

grad

eav

erag

e.E

ach

ofth

ese

vari

able

sex

cept

Eng

lish

lear

ner

stat

usis

fully

inte

ract

edw

ithgr

ade

fixed

effe

cts

and

aco

ntro

lfor

clas

ssi

zeis

incl

uded

.A

dditi

onal

cont

rols

incl

ude

fixed

effe

cts

for

the

num

ber

ofco

urse

sea

chst

uden

ttak

esin

high

scho

olin

four

mat

htr

acks

,num

ber

ofm

ath

AP

clas

ses,

num

ber

ofE

nglis

hA

Pcl

asse

s,nu

mbe

rof

Eng

lish

elec

tives

and

num

bero

f“he

lper

”E

nglis

hC

lass

es.S

tand

ard

erro

rscl

uste

red

atth

em

odal

high

scho

olle

vela

rere

port

edin

pare

nthe

ses.

***

p<

0.01

, **

p<

0.05

,and

*p

<0.

10.

85

Tabl

eA

.18:

Eff

ecto

fEle

men

tary

Scho

olTe

ache

rVal

ue-A

dded

Mea

sure

son

Add

ition

alH

igh

Scho

olO

utco

mes

Pool

edG

rade

s3-

5VA

LA

USD

Gra

duat

eTo

okPS

AT

PSA

TSc

ore

Gra

de11

Mat

hC

ST

Gra

de11

Eng

lish

CST

Gra

de8

Scie

nce

CST

Gra

de10

Scie

nce

CST

Gra

de8

Soci

al

Scie

nce

CST

Gra

de11

Soci

al

Scie

nce

CST

Wor

ld

His

tory

CST

Num

bero

f

AP

Cla

sses

Test

Scor

eVA

0.00

00.

002

4.97

6***

0.01

6***

0.01

5***

0.01

7***

0.01

7***

0.01

8***

0.01

3***

0.01

4***

0.00

7*

(0.0

01)

(0.0

01)

(0.6

42)

(0.0

05)

(0.0

03)

(0.0

03)

(0.0

03)

(0.0

03)

(0.0

04)

(0.0

03)

(0.0

04)

Beh

avio

rVA

0.00

1-0

.002

1.53

00.

005

0.00

30.

008*

0.00

50.

000

0.00

60.

006

0.01

4

(0.0

02)

(0.0

03)

(1.3

12)

(0.0

10)

(0.0

06)

(0.0

04)

(0.0

06)

(0.0

04)

(0.0

06)

(0.0

05)

(0.0

09)

Lea

rnin

gSk

ills

VA-0

.001

0.00

0-0

.329

-0.0

08-0

.006

0.00

1-0

.008

0.00

3-0

.013

**-0

.006

-0.0

06

(0.0

02)

(0.0

03)

(1.0

16)

(0.0

10)

(0.0

05)

(0.0

05)

(0.0

05)

(0.0

05)

(0.0

05)

(0.0

04)

(0.0

08)

Obs

erva

tions

64,0

3922

8,10

318

4,89

448

,330

53,4

4833

1,19

113

6,92

227

3,64

154

,880

113,

571

316,

116

R-s

quar

ed0.

048

0.05

30.

591

0.36

70.

479

0.47

60.

420

0.48

30.

357

0.36

60.

234

Not

e:T

hesa

mpl

ein

clud

esst

uden

tsin

grad

es3-

5w

hoat

tend

edhi

ghsc

hool

inth

eL

AU

SD.T

heun

itof

obse

rvat

ion

isa

stud

ent-

acad

emic

year

.Thi

sta

ble

repo

rts

the

effe

ctof

ast

anda

rdde

viat

ion

incr

ease

inth

eth

ree

mea

sure

sof

elem

enta

rysc

hool

teac

herv

alue

-add

ed(s

eeTa

ble

4)on

stud

ents

’hig

hsc

hool

outc

omes

.Spe

cific

ally

,eac

hco

lum

nof

the

tabl

ere

port

sth

eco

effic

ient

son

each

ofth

eth

ree

norm

aliz

edm

easu

res

ofte

ache

rva

lue-

adde

dfr

oman

OL

Sre

gres

sion

ofth

est

uden

ts’

high

scho

olou

tcom

eon

the

thre

em

easu

res

ofte

ache

rva

lue-

adde

dfo

rth

est

uden

ts’

teac

hers

ingr

ades

3,4,

or5,

alon

gw

ithth

eba

selin

eco

ntro

lsde

scri

bed

inse

ctio

n4.

1.T

heba

selin

eco

ntro

lsin

clud

ela

gsof

acu

bic

poly

nom

ialo

fthe

stud

ent’s

mat

hte

stsc

ore,

Eng

lish

test

scor

e,G

PA,a

ndef

fort

GPA

;acu

bic

poly

nom

ials

ofla

gged

clas

san

dgr

ade

leve

lmea

nsof

each

ofth

ose

vari

able

s;an

dcu

rren

tEng

lish

lear

ners

tatu

san

dla

gged

log

days

abse

nt,s

uspe

nsio

ns,a

ndhe

ldba

ckfo

rthe

indi

vidu

al,t

hecl

ass

aver

age,

and

the

grad

eav

erag

e.E

ach

ofth

ese

vari

able

sex

cept

Eng

lish

lear

ners

tatu

sis

fully

inte

ract

edw

ithgr

ade

fixed

effe

cts

and

aco

ntro

lfor

clas

ssi

zeis

incl

uded

.Sta

ndar

der

rors

clus

tere

dat

the

mod

alhi

ghsc

hool

leve

lare

repo

rted

inpa

rent

hese

s.**

*p

<0.

01, *

*p

<0.

05,a

nd*

p<

0.10

.

86

Tabl

eA

.19:

Eff

ecto

fEle

men

tary

Scho

olTe

ache

rVal

ue-A

dded

Est

imat

edw

ithTe

ache

rFix

edE

ffec

tson

Hig

hSc

hool

Out

com

es

Pool

edG

rade

s3-

5VA

LA

USD

Dro

pout

Took

SAT

SAT

Scor

eG

PAE

ffor

tGPA

Coo

pera

tion

GPA

Mat

h

CA

HSE

E

Eng

lish

CA

HSE

E

Day

s

Susp

ende

d

Log

Abs

ence

sH

eld

Bac

k

Test

Scor

eVA

-0.0

03-0

.002

9.27

3***

0.00

6**

0.00

5***

0.00

8***

0.03

0***

0.02

3***

0.00

1-0

.001

0.00

1

(0.0

02)

(0.0

03)

(1.5

04)

(0.0

03)

(0.0

02)

(0.0

01)

(0.0

03)

(0.0

03)

(0.0

02)

(0.0

04)

(0.0

02)

Beh

avio

rVA

-0.0

05**

0.01

0***

2.26

90.

018*

**0.

010*

**0.

007*

**0.

016*

**0.

005

-0.0

03-0

.020

***

-0.0

06**

(0.0

02)

(0.0

04)

(2.1

26)

(0.0

04)

(0.0

03)

(0.0

02)

(0.0

05)

(0.0

05)

(0.0

02)

(0.0

05)

(0.0

03)

Lea

rnin

gSk

ills

VA-0

.001

-0.0

00-0

.395

-0.0

02-0

.004

-0.0

04-0

.006

0.00

1-0

.003

0.00

40.

002

(0.0

02)

(0.0

03)

(1.9

48)

(0.0

06)

(0.0

03)

(0.0

03)

(0.0

05)

(0.0

04)

(0.0

02)

(0.0

07)

(0.0

03)

Obs

erva

tions

135,

786

102,

517

60,6

9429

3,02

123

3,07

823

3,07

815

2,34

515

1,82

031

6,11

627

7,33

122

1,75

7

R-s

quar

ed0.

293

0.14

50.

617

0.24

40.

234

0.23

90.

501

0.51

20.

040

0.26

70.

108

Not

e:T

hesa

mpl

ein

clud

esst

uden

tsin

grad

es3-

5w

hoat

tend

edhi

ghsc

hool

inth

eL

AU

SD.T

heun

itof

obse

rvat

ion

isa

stud

ent-

acad

emic

year

.Thi

sta

ble

repo

rts

the

effe

ctof

ast

anda

rdde

viat

ion

incr

ease

inth

eth

ree

mea

sure

sof

elem

enta

rysc

hool

teac

herv

alue

-add

ed(s

eeTa

ble

4)on

stud

ents

’hig

hsc

hool

outc

omes

.The

valu

e-ad

ded

estim

ates

used

inth

eth

ree

indi

ces

are

estim

ated

usin

gC

hetty

,Fri

edm

an,

and

Roc

koff

’s(2

014a

,201

4b)a

ppro

ach,

whi

ches

timat

esth

ere

latio

nshi

pbe

twee

nco

ntro

lsan

dst

uden

tach

ieve

men

tusi

ngw

ithin

-tea

cher

vari

atio

n.Sp

ecifi

cally

,equ

atio

n(1

)is

mod

ified

toin

clud

ea

teac

her

fixed

effe

ct,b

uteq

uatio

n(3

)is

unch

ange

d.E

ach

colu

mn

ofth

eta

ble

repo

rts

the

coef

ficie

nts

onea

chof

the

thre

eno

rmal

ized

mea

sure

sof

teac

herv

alue

-add

edfr

oman

OL

Sre

gres

sion

ofth

est

uden

ts’h

igh

scho

olou

tcom

eon

the

thre

em

easu

res

ofte

ache

rval

ue-a

dded

fort

hest

uden

ts’t

each

ers

ingr

ades

3,4,

or5,

alon

gw

ithth

eba

selin

eco

ntro

lsde

scri

bed

inse

ctio

n4.

1.T

heba

selin

eco

ntro

lsin

clud

ela

gsof

acu

bic

poly

nom

ialo

fthe

stud

ent’s

mat

hte

stsc

ore,

Eng

lish

test

scor

e,G

PA,a

ndef

fort

GPA

;acu

bic

poly

nom

ials

ofla

gged

clas

san

dgr

ade

leve

lmea

nsof

each

ofth

ose

vari

able

s;an

dcu

rren

tEng

lish

lear

ner

stat

usan

dla

gged

log

days

abse

nt,s

uspe

nsio

ns,a

ndhe

ldba

ckfo

rthe

indi

vidu

al,t

hecl

ass

aver

age,

and

the

grad

eav

erag

e.E

ach

ofth

ese

vari

able

sex

cept

Eng

lish

lear

ners

tatu

sis

fully

inte

ract

edw

ithgr

ade

fixed

effe

cts

and

aco

ntro

lfor

clas

ssi

zeis

incl

uded

.Sta

ndar

der

rors

clus

tere

dat

the

mod

alhi

ghsc

hool

leve

lare

repo

rted

inpa

rent

hese

s.**

*p

<0.

01, *

*p

<0.

05,a

nd*

p<

0.10

.

87

Tabl

eA

.20:

Eff

ecto

fEle

men

tary

Scho

olTe

ache

rVal

ue-A

dded

onH

igh

Scho

olO

utco

mes

Est

imat

edU

sing

With

in-T

each

erV

aria

tion

Pool

edG

rade

s3-

5VA

LA

USD

Dro

pout

Took

SAT

SAT

Scor

eG

PAE

ffor

tGPA

Coo

pera

tion

GPA

Mat

h

CA

HSE

E

Eng

lish

CA

HSE

E

Day

s

Susp

ende

d

Log

Abs

ence

sH

eld

Bac

k

Test

Scor

eVA

-0.0

17**

*0.

002

27.7

88**

*0.

026*

**0.

019*

**0.

024*

**0.

052*

**0.

050*

**-0

.002

-0.0

14**

*-0

.001

(0.0

04)

(0.0

03)

(4.0

85)

(0.0

04)

(0.0

03)

(0.0

04)

(0.0

07)

(0.0

05)

(0.0

02)

(0.0

05)

(0.0

02)

Beh

avio

rVA

-0.0

010.

013*

**3.

267

0.02

2***

0.01

2***

0.00

9**

0.02

6***

0.00

9-0

.005

**-0

.017

***

-0.0

07**

(0.0

04)

(0.0

04)

(3.3

82)

(0.0

05)

(0.0

04)

(0.0

04)

(0.0

08)

(0.0

06)

(0.0

02)

(0.0

06)

(0.0

03)

Lea

rnin

gSk

ills

VA-0

.012

**0.

000

7.42

9*0.

005

0.00

0-0

.001

-0.0

000.

012*

-0.0

04-0

.002

0.00

1

(0.0

05)

(0.0

04)

(4.2

48)

(0.0

07)

(0.0

05)

(0.0

05)

(0.0

07)

(0.0

07)

(0.0

02)

(0.0

09)

(0.0

03)

Obs

erva

tions

135,

786

102,

517

60,6

9429

3,02

123

3,07

823

3,07

815

2,34

515

1,82

031

6,11

627

7,33

122

1,75

7

R-s

quar

ed0.

003

0.00

10.

029

0.00

20.

003

0.00

50.

009

0.00

80.

000

0.00

10.

000

Not

e:T

hesa

mpl

ein

clud

esst

uden

tsin

grad

es3-

5w

hoat

tend

edhi

ghsc

hool

inth

eL

AU

SD.T

heun

itof

obse

rvat

ion

isa

stud

ent-

acad

emic

year

.Thi

sta

ble

repo

rts

the

effe

ctof

ast

anda

rdde

viat

ion

incr

ease

inth

eth

ree

mea

sure

sof

elem

enta

rysc

hool

teac

herv

alue

-add

ed(s

eeTa

ble

4)on

stud

ents

’hig

hsc

hool

outc

omes

.The

valu

e-ad

ded

estim

ates

used

inth

eth

ree

indi

ces

are

estim

ated

usin

gth

eC

hetty

,Fri

edm

an,

and

Roc

koff

(201

4a,2

014b

)app

roac

h,w

hich

estim

ates

the

rela

tions

hip

betw

een

cont

rols

and

stud

enta

chie

vem

entu

sing

with

in-t

each

erva

riat

ion.

Spec

ifica

lly,e

quat

ion

(1)i

sm

odifi

edto

incl

ude

ate

ache

rfix

edef

fect

,but

equa

tion

(3)i

sun

chan

ged.

The

outc

ome

vari

able

sar

ere

sidu

aliz

edus

ing

the

sam

eap

proa

ch.E

ach

colu

mn

ofth

eta

ble

repo

rts

the

coef

ficie

nts

onea

chof

the

thre

eno

rmal

ized

mea

sure

sof

teac

herv

alue

-add

edfr

oman

OL

Sre

gres

sion

ofth

est

uden

ts’r

esid

ualiz

edhi

ghsc

hool

outc

ome

onth

eth

ree

mea

sure

sof

teac

herv

alue

-add

edfo

rthe

stud

ents

’tea

cher

sin

grad

es3,

4,or

5(a

ndno

cont

rols

).St

anda

rder

rors

clus

tere

dat

the

mod

alhi

ghsc

hool

leve

lare

repo

rted

inpa

rent

hese

s.**

*p

<0.

01, *

*p

<0.

05,a

nd*

p<

0.10

.

88

Tabl

eA

.21:

Lon

g-Te

rmE

ffec

toft

heTe

ache

rVal

ue-A

dded

Mea

sure

sE

stim

ated

Usi

nga

Fact

orM

odel

Pool

edG

rade

s3-

5VA

LA

USD

Dro

pout

Took

SAT

SAT

Scor

eG

PAE

ffor

tGPA

Coo

pera

tion

GPA

Mat

h

CA

HSE

E

Eng

lish

CA

HSE

E

Day

s

Susp

ende

d

Log

Abs

ence

sH

eld

Bac

k

Test

Scor

eVA

-0.0

02-0

.002

6.18

7***

0.00

20.

003*

0.00

5***

0.02

2***

0.01

6***

0.00

10.

001

0.00

1

(0.0

02)

(0.0

02)

(1.2

74)

(0.0

02)

(0.0

01)

(0.0

01)

(0.0

03)

(0.0

03)

(0.0

01)

(0.0

03)

(0.0

01)

Beh

avio

rVA

-0.0

040.

013*

**2.

336

0.01

3**

0.00

7**

0.00

40.

015*

*0.

006

-0.0

03-0

.021

***

-0.0

05*

(0.0

03)

(0.0

04)

(2.7

34)

(0.0

05)

(0.0

03)

(0.0

03)

(0.0

07)

(0.0

06)

(0.0

02)

(0.0

06)

(0.0

03)

Lea

rnin

gSk

ills

VA0.

000

-0.0

04-0

.790

-0.0

03-0

.003

-0.0

03-0

.008

-0.0

01-0

.001

0.00

50.

001

(0.0

03)

(0.0

03)

(1.8

93)

(0.0

05)

(0.0

03)

(0.0

03)

(0.0

05)

(0.0

04)

(0.0

02)

(0.0

06)

(0.0

03)

Obs

erva

tions

135,

786

102,

517

60,6

9429

3,02

123

3,07

823

3,07

815

2,34

515

1,82

031

6,11

627

7,33

122

1,75

7

R-s

quar

ed0.

293

0.14

50.

617

0.24

40.

234

0.23

90.

500

0.51

20.

040

0.26

70.

108

Not

e:T

hesa

mpl

ein

clud

esst

uden

tsin

grad

es3-

5w

hoat

tend

edhi

ghsc

hool

inth

eL

AU

SD.T

heun

itof

obse

rvat

ion

isa

stud

ent-

acad

emic

year

.Thi

sta

ble

repo

rts

the

effe

ctof

ast

anda

rdde

viat

ion

incr

ease

inth

eth

ree

elem

enta

rysc

hool

teac

her

valu

e-ad

ded

mea

sure

s(c

reat

edus

ing

afa

ctor

mod

el)

onst

uden

ts’

high

scho

olou

tcom

es.

Eac

hco

lum

nof

the

tabl

ere

port

sth

eco

effic

ient

son

each

ofth

eth

ree

norm

aliz

edm

easu

res

ofte

ache

rval

ue-a

dded

from

anO

LS

regr

essi

onof

the

stud

ents

’hig

hsc

hool

outc

ome

onth

eth

ree

mea

sure

sof

teac

herv

alue

-add

edfo

rthe

stud

ents

’tea

cher

sin

grad

es3,

4,or

5,al

ong

with

the

base

line

cont

rols

desc

ribe

din

sect

ion

4.1.

The

base

line

cont

rols

incl

ude

lags

ofa

cubi

cpo

lyno

mia

loft

hest

uden

t’sm

ath

test

scor

e,E

nglis

hte

stsc

ore,

GPA

,and

effo

rtG

PA;a

cubi

cpo

lyno

mia

lsof

lagg

edcl

ass

and

grad

ele

velm

eans

ofea

chof

thos

eva

riab

les;

and

curr

entE

nglis

hle

arne

rsta

tus

and

lagg

edlo

gda

ysab

sent

,sus

pens

ions

,and

held

back

fort

hein

divi

dual

,the

clas

sav

erag

e,an

dth

egr

ade

aver

age.

Eac

hof

thes

eva

riab

les

exce

ptE

nglis

hle

arne

rsta

tus

isfu

llyin

tera

cted

with

grad

efix

edef

fect

san

da

cont

rolf

orcl

ass

size

isin

clud

ed.S

tand

ard

erro

rscl

uste

red

atth

em

odal

high

scho

olle

vela

rere

port

edin

pare

nthe

ses.

The

fact

orlo

ads

are

repo

rted

inTa

ble

A.2

2.**

*p

<0.

01, *

*p

<0.

05,a

nd*

p<

0.10

.

89

Table A.22: Factor Loads for Elementary School Value-Added

Grades 3-5 VA Test Score BehaviorLearning

Skills

Math Test Scores 0.804

English Test Scores 0.804

GPA 0.253

Log Absences -0.271

Suspended -0.160

Held Back -0.049

Effort GPA 0.798

Work and Study GPA 0.917

Learning and Social GPA 0.886

Note: This table reports the factor loads for the test-score, behavior, and learning-skills teacher valued-added measures in elementary school (grades3-5). Each of the three measures of teacher value-added is created using factor analysis. The test-score value-added is computed using the teachers’value-added for math and English test scores, and the factor loads are shown in column 1. The behavior value-added is computed using teachers’value-added for GPA, suspensions, log days absent, and not progressing to the next grade on time (held back), and the factor loads are shown incolumn 2. The learning-skills value-added is computed using teachers’ value-added for effort GPA, work and study habit GPA, and learning andsocial skills GPA, and the factor loads are shown in column 3.

90

Tabl

eA

.23:

Eff

ecto

fEle

men

tary

Scho

olTe

ache

rVal

ue-A

dded

onH

igh

Scho

olO

utco

mes

Usi

ngE

xplo

rato

ryFa

ctor

Ana

lysi

s

Pool

edG

rade

s3-

5VA

LA

USD

Dro

pout

Took

SAT

SAT

Scor

eG

PAE

ffor

tGPA

Coo

pera

tion

GPA

Mat

h

CA

HSE

E

Eng

lish

CA

HSE

E

Day

s

Susp

ende

d

Log

Abs

ence

sH

eld

Bac

k

Fact

or1

(Loa

dsPr

imar

ily-0

.002

-0.0

016.

507*

**0.

004

0.00

3**

0.00

5***

0.02

3***

0.01

7***

0.00

1-0

.001

0.00

0

onTe

stSc

ores

)(0

.002

)(0

.002

)(1

.246

)(0

.002

)(0

.001

)(0

.001

)(0

.003

)(0

.002

)(0

.001

)(0

.003

)(0

.001

)

Fact

or2

(Loa

dsPr

imar

ily-0

.002

0.00

4*1.

394

0.00

40.

001

-0.0

000.

003

0.00

5-0

.003

*-0

.006

-0.0

02

onN

on-t

estS

core

)(0

.002

)(0

.002

)(2

.039

)(0

.005

)(0

.003

)(0

.003

)(0

.005

)(0

.004

)(0

.002

)(0

.006

)(0

.002

)

Obs

erva

tions

135,

786

102,

517

60,6

9429

3,02

123

3,07

823

3,07

815

2,34

515

1,82

031

6,11

627

7,33

122

1,75

7

R-s

quar

ed0.

293

0.14

50.

617

0.24

40.

234

0.23

90.

500

0.51

20.

040

0.26

60.

108

Not

e:T

hesa

mpl

ein

clud

esst

uden

tsin

grad

es3-

5w

hoat

tend

edhi

ghsc

hool

inth

eL

AU

SD.T

heun

itof

obse

rvat

ion

isa

stud

ent-

acad

emic

year

.Thi

sta

ble

repo

rts

the

effe

ctof

ast

anda

rdde

viat

ion

incr

ease

inth

etw

oal

tern

ativ

em

easu

res

ofel

emen

tary

scho

olte

ache

rval

ue-a

dded

onst

uden

ts’h

igh

scho

olou

tcom

es.T

hetw

ofa

ctor

sar

eco

mpu

ted

usin

ga

mod

elw

ithal

lofo

urm

easu

res

ofte

ache

rval

ue-a

dded

incl

uded

sim

ulta

neou

sly

rath

erth

anby

impo

sing

that

only

cert

ain

valu

e-ad

ded

mea

sure

sco

ntri

bute

toce

rtai

nfa

ctor

s.T

heva

lue-

adde

dm

easu

res

used

toco

mpu

teth

efa

ctor

sin

clud

em

ath

and

Eng

lish

test

scor

es,s

uspe

nsio

ns,l

ogda

ysab

sent

,not

prog

ress

ing

toth

ene

xtgr

ade

ontim

e(h

eld

back

),G

PA,e

ffor

tGPA

,wor

kan

dst

udy

habi

tsG

PA,a

ndle

arni

ngan

dso

cial

skill

sG

PA.T

hese

cond

fact

orpr

imar

ilylo

ads

onth

etw

ote

st-s

core

valu

e-ad

ded

mea

sure

san

dto

ale

sser

exte

ntG

PA,w

here

asth

efir

stfa

ctor

load

spr

imar

ilyon

GPA

and

the

othe

rno

n-te

st-s

core

vari

able

s.E

ach

colu

mn

ofth

eta

ble

repo

rts

the

coef

ficie

nts

onea

chof

the

two

alte

rnat

ive

mea

sure

sof

teac

her

valu

e-ad

ded

from

anO

LS

regr

essi

onof

the

stud

ents

’hi

ghsc

hool

outc

ome

onth

etw

oal

tern

ativ

em

easu

res

ofte

ache

rva

lue-

adde

dfo

rth

est

uden

ts’t

each

ers

ingr

ades

3,4,

or5,

alon

gw

ithth

eba

selin

eco

ntro

lsde

scri

bed

inse

ctio

n4.

1.T

heba

selin

eco

ntro

lsin

clud

ela

gsof

acu

bic

poly

nom

ialo

fthe

stud

ent’s

mat

hte

stsc

ore,

Eng

lish

test

scor

e,G

PA,a

ndef

fort

GPA

;acu

bic

poly

nom

ials

ofla

gged

clas

san

dgr

ade

leve

lmea

nsof

each

ofth

ose

vari

able

s;an

dcu

rren

tEng

lish

lear

ner

stat

usan

dla

gged

log

days

abse

nt,s

uspe

nsio

ns,a

ndhe

ldba

ckfo

rth

ein

divi

dual

,the

clas

sav

erag

e,an

dth

egr

ade

aver

age.

Eac

hof

thes

eva

riab

les

exce

ptE

nglis

hle

arne

rst

atus

isfu

llyin

tera

cted

with

grad

efix

edef

fect

san

da

cont

rolf

orcl

ass

size

isin

clud

ed.

Stan

dard

erro

rscl

uste

red

atth

em

odal

high

scho

olle

vela

rere

port

edin

pare

nthe

ses.

***

p<

0.01

, **

p<

0.05

,and

*p

<0.

10.

91

Table A.24: Factor Loads for the Exploratory Factor Analysis

Grades 3-5 VA Factor 1 Factor 2

Math Test Scores 0.102 0.807

English Test Scores 0.118 0.808

GPA 0.781 0.126

Log Absences 0.121 0.044

Suspended 0.065 0.029

Held Back 0.008 0.024

Effort GPA 0.845 0.068

Work and Study GPA 0.910 0.088

Learning and Social GPA 0.866 0.050

Note: This table reports the factor loads for the two factors used in Table A.23. The two factors of teacher value-added measures are created usingfactor analysis. All nine of the listed value-added measures are variables included in the factor analysis. Factors with an eigenvalue grater than oneare included.

92

Tabl

eA

.25:

Lon

g-Te

rmE

ffec

toft

heTe

ache

rVal

ue-A

dded

Mea

sure

sE

stim

ated

inPe

rcen

tiles

Pool

edG

rade

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5VA

LA

USD

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pout

Took

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SAT

Scor

e

Perc

entil

e

GPA

Perc

entil

e

Eff

ortG

PA

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entil

e

Coo

pera

tion

GPA

Perc

entil

e

Mat

h

CA

HSE

E

Perc

entil

e

Eng

lish

CA

HSE

E

Perc

entil

e

Day

s

Susp

ende

d

Log

Abs

ence

sH

eld

Bac

k

Test

Scor

eVA

Dec

ile-0

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6-0

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20.

0018

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003

0.00

00-0

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00.

0199

***

0.00

97**

*-0

.020

7-0

.037

30.

0064

(0.0

178)

(0.0

183)

(0.0

012)

(0.0

003)

(0.0

002)

(0.0

002)

(0.0

039)

(0.0

021)

(0.0

168)

(0.0

312)

(0.0

138)

Beh

avio

rVA

Dec

ile-0

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50.

1065

***

0.00

340.

0003

0.00

020.

0004

0.01

78*

0.00

57-0

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9-0

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0*-0

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8***

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418)

(0.0

389)

(0.0

025)

(0.0

009)

(0.0

014)

(0.0

014)

(0.0

101)

(0.0

057)

(0.0

333)

(0.0

690)

(0.0

353)

Lea

rnin

gSk

ills

VAD

ecile

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079

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74-0

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0032

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0036

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001

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027

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187*

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119

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110)

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013)

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001)

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023)

(0.0

022)

(0.0

022)

(0.0

010)

(0.0

039)

(0.0

107)

(0.0

087)

Obs

erva

tions

135,

786

102,

517

60,6

9429

3,02

123

3,07

823

3,07

815

2,34

515

1,82

031

6,11

627

7,33

122

1,75

7

R-s

quar

ed0.

293

0.14

50.

225

0.17

80.

978

0.97

80.

158

0.14

20.

039

0.26

70.

108

Not

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mpl

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uden

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ent-

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esh

ows

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ctof

ade

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ease

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ree

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enta

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her

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ces

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omes

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ache

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nal

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rent

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port

edin

Tabl

eA

.22.

***

p<

0.01

, **

p<

0.05

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<0.

10.

93

Tabl

eA

.26:

Eff

ecto

fthe

Ele

men

tary

Scho

olTe

ache

rVal

ue-A

dded

Mea

sure

son

Hig

hSc

hool

Out

com

esw

ithou

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avio

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ue-A

dded

Pool

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pera

tion

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h

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lish

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E

Day

s

Susp

ende

d

Log

Abs

ence

sH

eld

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k

Test

Scor

eVA

-0.0

02-0

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6.36

4***

0.00

30.

003*

*0.

005*

**0.

023*

**0.

017*

**0.

001

-0.0

000.

001

(0.0

02)

(0.0

02)

(1.2

51)

(0.0

02)

(0.0

01)

(0.0

01)

(0.0

03)

(0.0

03)

(0.0

01)

(0.0

03)

(0.0

01)

Lea

rnin

gSk

ills

VA-0

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0.00

30.

391

0.00

40.

000

-0.0

010.

000

0.00

2-0

.003

*-0

.005

-0.0

02

(0.0

02)

(0.0

02)

(1.9

66)

(0.0

05)

(0.0

03)

(0.0

03)

(0.0

05)

(0.0

04)

(0.0

02)

(0.0

06)

(0.0

02)

Obs

erva

tions

136,

236

102,

901

60,9

1529

3,77

023

3,69

623

3,69

615

2,80

315

2,27

231

6,96

627

8,05

122

2,33

5

R-s

quar

ed0.

293

0.14

50.

617

0.24

40.

234

0.23

90.

500

0.51

20.

040

0.26

60.

108

Not

e:T

his

tabl

ere

port

sth

eef

fect

ofa

stan

dard

devi

atio

nin

crea

sein

test

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rean

dle

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ng-s

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enta

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hool

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her

valu

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ded

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Tabl

e4)

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scho

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es.

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ifica

lly,

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ofth

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PA,a

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ptE

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atus

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llyin

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cted

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grad

efix

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fect

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da

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ass

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tand

ard

erro

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red

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odal

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port

edin

pare

nthe

ses.

***

p<

0.01

, **

p<

0.05

,and

*p

<0.

10.

94

Tabl

eA

.27:

Eff

ect

ofE

lem

enta

rySc

hool

Teac

her

Val

ue-A

dded

onH

igh

Scho

olO

utco

mes

-E

xclu

ding

Susp

ende

dan

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eld

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kE

lem

enta

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rade

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lish

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Day

s

Susp

ende

d

Log

Abs

ence

sH

eld

Bac

k

Test

Scor

eVA

-0.0

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6.20

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10.

002

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0.02

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0.00

10.

001

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1

(0.0

02)

(0.0

02)

(1.2

97)

(0.0

02)

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01)

(0.0

01)

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03)

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03)

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01)

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03)

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01)

Beh

avio

rVA

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030.

010*

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984

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004

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03-0

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***

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06**

(0.0

02)

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03)

(2.1

55)

(0.0

04)

(0.0

03)

(0.0

02)

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06)

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05)

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02)

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05)

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03)

Lea

rnin

gSk

ills

VA-0

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00.

001

(0.0

02)

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03)

(1.8

20)

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05)

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03)

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03)

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05)

(0.0

04)

(0.0

02)

(0.0

06)

(0.0

03)

Obs

erva

tions

129,

193

98,0

9958

,529

278,

453

221,

690

221,

690

145,

703

145,

228

299,

648

263,

003

211,

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R-s

quar

ed0.

300

0.14

40.

617

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10.

230

0.23

40.

500

0.51

20.

035

0.26

90.

107

Not

e:T

hesa

mpl

ein

clud

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sta

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ctof

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cific

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ree

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est

uden

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hers

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ades

3,4,

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n4.

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enta

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ath

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ore,

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tere

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rent

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s.**

*p

<0.

01, *

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95

Tabl

eA

.28:

Eff

ecto

fEle

men

tary

Scho

olTe

ache

rVal

ue-A

dded

onH

igh

Scho

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mes

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Day

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Log

Abs

ence

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eld

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k

Test

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eVA

(t+

1)-0

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***

0.00

111

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0.01

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01-0

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02

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02)

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03)

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06)

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04)

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03)

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03)

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05)

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04)

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02)

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05)

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02)

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avio

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010*

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009*

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02)

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03)

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43)

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04)

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02)

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02)

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05)

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04)

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02)

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03)

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ills

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000

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002

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1

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02)

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03)

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57)

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05)

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03)

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03)

(0.0

04)

(0.0

03)

(0.0

02)

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06)

(0.0

03)

Obs

erva

tions

131,

999

99,6

2259

,004

285,

915

227,

272

227,

272

148,

368

147,

863

308,

344

270,

602

216,

288

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617

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234

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90.

500

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039

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108

Not

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ctof

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ient

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ithth

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eco

ntro

lsde

scri

bed

inse

ctio

n4.

1.Te

st-s

core

valu

e-ad

ded

ises

timat

edus

ing

test

scor

esin

year

t+1.

The

base

line

cont

rols

incl

ude

lags

ofa

cubi

cpo

lyno

mia

lof

the

stud

ent’s

mat

hte

stsc

ore,

Eng

lish

test

scor

e,G

PA,a

ndef

fort

GPA

;acu

bic

poly

nom

ials

ofla

gged

clas

san

dgr

ade

leve

lmea

nsof

each

ofth

ose

vari

able

s;an

dcu

rren

tEng

lish

lear

ners

tatu

san

dla

gged

log

days

abse

nt,s

uspe

nsio

ns,

and

held

back

for

the

indi

vidu

al,t

hecl

ass

aver

age,

and

the

grad

eav

erag

e.E

ach

ofth

ese

vari

able

sex

cept

Eng

lish

lear

ner

stat

usis

fully

inte

ract

edw

ithgr

ade

fixed

effe

cts

and

aco

ntro

lfor

clas

ssi

zeis

incl

uded

.Eac

hof

thes

eva

riab

les

exce

ptE

nglis

hle

arne

rsta

tus

isfu

llyin

tera

cted

with

grad

efix

edef

fect

san

da

cont

rolf

orcl

ass

size

isin

clud

ed.S

tand

ard

erro

rscl

uste

red

atth

em

odal

high

scho

olle

vela

rere

port

edin

pare

nthe

ses.

***

p<

0.01

, **

p<

0.05

,and

*p

<0.

10.

96

Tabl

eA

.29:

Eff

ecto

fEle

men

tary

Scho

olTe

ache

rVal

ue-A

dded

onH

igh

Scho

olO

utco

mes

-Con

tem

pora

neou

sVA

Pool

edG

rade

s3-

5VA

LA

USD

Dro

pout

Took

SAT

SAT

Scor

eG

PAE

ffor

tGPA

Coo

pera

tion

GPA

Mat

h

CA

HSE

E

Eng

lish

CA

HSE

E

Day

s

Susp

ende

d

Log

Abs

ence

sH

eld

Bac

k

Test

Scor

eVA

-0.0

02-0

.001

6.51

5***

0.00

30.

003*

0.00

4***

0.02

3***

0.01

7***

-0.0

00-0

.001

0.00

1

(0.0

02)

(0.0

02)

(1.0

70)

(0.0

02)

(0.0

01)

(0.0

01)

(0.0

03)

(0.0

02)

(0.0

01)

(0.0

02)

(0.0

01)

Beh

avio

rVA

(t)

-0.0

030.

003

0.29

20.

008*

*0.

004*

*0.

003*

0.00

50.

001

0.00

2-0

.011

***

-0.0

03

(0.0

02)

(0.0

03)

(1.4

63)

(0.0

03)

(0.0

02)

(0.0

02)

(0.0

04)

(0.0

03)

(0.0

02)

(0.0

04)

(0.0

02)

Lea

rnin

gSk

ills

VA(t)

0.00

1-0

.000

-2.0

25-0

.006

*-0

.005

***

-0.0

04**

-0.0

07*

-0.0

06**

-0.0

010.

003

0.00

2*

(0.0

02)

(0.0

02)

(1.3

07)

(0.0

03)

(0.0

02)

(0.0

02)

(0.0

04)

(0.0

03)

(0.0

02)

(0.0

03)

(0.0

01)

Obs

erva

tions

179,

761

136,

115

81,6

8436

6,71

929

6,22

329

6,22

319

7,75

919

7,07

639

6,27

334

7,59

028

1,58

8

R-s

quar

ed0.

293

0.15

20.

631

0.25

50.

245

0.24

90.

512

0.52

60.

040

0.26

50.

112

Not

e:T

hesa

mpl

ein

clud

esst

uden

tsin

grad

es3-

5w

hoat

tend

edhi

ghsc

hool

inth

eL

AU

SD.T

heun

itof

obse

rvat

ion

isa

stud

ent-

acad

emic

year

.Thi

sta

ble

repo

rts

the

effe

ctof

ast

anda

rdde

viat

ion

incr

ease

inth

eth

ree

mea

sure

sof

elem

enta

rysc

hool

teac

herv

alue

-add

ed(s

eeTa

ble

4)on

stud

ents

’hig

hsc

hool

outc

omes

.Spe

cific

ally

,eac

hco

lum

nof

the

tabl

ere

port

sth

eco

effic

ient

son

each

ofth

eth

ree

norm

aliz

edm

easu

res

ofte

ache

rva

lue-

adde

dfr

oman

OL

Sre

gres

sion

ofth

est

uden

ts’

high

scho

olou

tcom

eon

the

thre

em

easu

res

ofte

ache

rva

lue-

adde

dfo

rth

est

uden

ts’

teac

hers

ingr

ades

3,4,

or5,

alon

gw

ithth

eba

selin

eco

ntro

lsde

scri

bed

inse

ctio

n4.

1.B

ehav

iora

ndle

arni

ngsk

ills

valu

e-ad

ded

wer

eco

nstr

ucte

dus

ing

year

tra

ther

than

year

t+1

outc

omes

.The

base

line

cont

rols

incl

ude

lags

ofa

cubi

cpo

lyno

mia

lof

the

stud

ent’s

mat

hte

stsc

ore,

Eng

lish

test

scor

e,G

PA,a

ndef

fort

GPA

;acu

bic

poly

nom

ials

ofla

gged

clas

san

dgr

ade

leve

lmea

nsof

each

ofth

ose

vari

able

s;an

dcu

rren

tEng

lish

lear

ners

tatu

san

dla

gged

log

days

abse

nt,s

uspe

nsio

ns,a

ndhe

ldba

ckfo

rthe

indi

vidu

al,t

hecl

ass

aver

age,

and

the

grad

eav

erag

e.E

ach

ofth

ese

vari

able

sex

cept

Eng

lish

lear

ners

tatu

sis

fully

inte

ract

edw

ithgr

ade

fixed

effe

cts

and

aco

ntro

lfor

clas

ssi

zeis

incl

uded

.Sta

ndar

der

rors

clus

tere

dat

the

mod

alhi

ghsc

hool

leve

lare

repo

rted

inpa

rent

hese

s.**

*p

<0.

01, *

*p

<0.

05,a

nd*

p<

0.10

.

97

Tabl

eA

.30:

Eff

ecto

fEle

men

tary

Scho

olTe

ache

rVal

ue-A

dded

onH

igh

Scho

olO

utco

mes

-Z-S

core

GPA

VAan

dO

utco

mes

Gra

des

3-5

VAG

PAz-

scor

e

GPA

Eff

ortG

PAz-

scor

e

Eff

ortG

PA

Coo

pera

tion

GPA

z-sc

ore

Coo

pera

tion

GPA

Test

Scor

eVA

0.00

20.

003

0.00

3*0.

005*

0.00

5***

0.01

0***

(0.0

02)

(0.0

02)

(0.0

01)

(0.0

03)

(0.0

01)

(0.0

03)

Beh

avio

rVA

(Z-G

PA)

0.01

3***

0.01

4***

0.00

7***

0.01

4***

0.00

5**

0.01

1**

(0.0

04)

(0.0

04)

(0.0

03)

(0.0

05)

(0.0

02)

(0.0

05)

Lea

rnin

gSk

ills

VA(Z

-GPA

)-0

.002

-0.0

02-0

.003

-0.0

06-0

.003

-0.0

08

(0.0

05)

(0.0

05)

(0.0

03)

(0.0

06)

(0.0

03)

(0.0

07)

Obs

erva

tions

293,

021

293,

021

233,

078

233,

078

233,

078

233,

078

R-s

quar

ed0.

244

0.24

40.

234

0.23

40.

239

0.23

9

Not

e:T

hesa

mpl

ein

clud

esst

uden

tsin

grad

es3-

5w

hoat

tend

edhi

ghsc

hool

inth

eL

AU

SD.T

heun

itof

obse

rvat

ion

isa

stud

ent-

acad

emic

year

.Thi

sta

ble

repo

rts

the

effe

ctof

ast

anda

rdde

viat

ion

incr

ease

inth

eth

ree

mea

sure

sof

elem

enta

rysc

hool

teac

herv

alue

-add

ed(s

eeTa

ble

4)on

stud

ents

’hig

hsc

hool

outc

omes

.Spe

cific

ally

,eac

hco

lum

nof

the

tabl

ere

port

sth

eco

effic

ient

son

each

ofth

eth

ree

norm

aliz

edm

easu

res

ofte

ache

rva

lue-

adde

dfr

oman

OL

Sre

gres

sion

ofth

est

uden

ts’

high

scho

olou

tcom

eon

the

thre

em

easu

res

ofte

ache

rva

lue-

adde

dfo

rth

est

uden

ts’

teac

hers

ingr

ades

3,4,

or5,

alon

gw

ithth

eba

selin

eco

ntro

lsde

scri

bed

inse

ctio

n4.

1.T

heG

PAva

riab

les

used

toco

nstr

uctt

heva

lue-

adde

dm

easu

res

wer

eco

nver

ted

toz-

scor

esbe

fore

estim

atin

gte

ache

rval

ue-a

dded

.T

heba

selin

eco

ntro

lsin

clud

ela

gsof

acu

bic

poly

nom

ialo

fth

est

uden

t’sm

ath

test

scor

e,E

nglis

hte

stsc

ore,

GPA

,and

effo

rtG

PA;a

cubi

cpo

lyno

mia

lsof

lagg

edcl

ass

and

grad

ele

velm

eans

ofea

chof

thos

eva

riab

les;

and

curr

ent

Eng

lish

lear

ner

stat

usan

dla

gged

log

days

abse

nt,s

uspe

nsio

ns,a

ndhe

ldba

ckfo

rth

ein

divi

dual

,the

clas

sav

erag

e,an

dth

egr

ade

aver

age.

Eac

hof

thes

eva

riab

les

exce

ptE

nglis

hle

arne

rst

atus

isfu

llyin

tera

cted

with

grad

efix

edef

fect

san

da

cont

rolf

orcl

ass

size

isin

clud

ed.S

tand

ard

erro

rscl

uste

red

atth

em

odal

high

scho

olle

vela

rere

port

edin

pare

nthe

ses.

***

p<

0.01

, **

p<

0.05

,and

*p

<0.

10.

98

Tabl

eA

.31:

The

Cum

ulat

ive

Eff

ecto

fGra

des

4-11

Teac

herV

alue

-Add

edM

easu

res

onH

igh

Scho

olO

utco

mes

Ave

rage

Gra

de

4-11

VA

LA

USD

Dro

pout

HS

Gra

duat

eTo

okSA

TSA

TSc

ore

12G

rade

GPA

12G

rade

Eff

ortG

PA

12G

rade

Coo

pera

tion

GPA

11G

rade

Mat

hTe

st

Scor

es

11G

rade

Eng

lish

Test

Scor

es

12G

rade

Day

s

Susp

ende

d

12G

rade

Log

Abs

ence

s

Hel

dB

ack

11G

rade

Avg

Test

Scor

eVA

-0.0

15*

0.01

3***

0.06

9***

57.4

71**

*0.

041*

0.03

9***

0.04

8***

0.48

9***

0.24

8***

0.00

0-0

.109

***

-0.0

09**

(0.0

07)

(0.0

05)

(0.0

16)

(5.6

28)

(0.0

23)

(0.0

11)

(0.0

11)

(0.0

29)

(0.0

21)

(0.0

02)

(0.0

34)

(0.0

04)

Avg

Beh

avio

rVA

-0.0

38**

*0.

012*

*0.

031*

**-3

0.03

3***

0.04

6**

0.04

6***

0.02

3*-0

.046

-0.0

41**

-0.0

02**

*-0

.202

***

-0.0

08*

(0.0

09)

(0.0

06)

(0.0

10)

(6.8

27)

(0.0

22)

(0.0

14)

(0.0

12)

(0.0

32)

(0.0

17)

(0.0

01)

(0.0

27)

(0.0

04)

Obs

erva

tions

36,3

7520

,318

30,8

8817

,377

28,4

4118

,001

18,0

0117

,001

18,3

7830

,888

28,3

0029

,572

R-s

quar

ed0.

537

0.02

70.

151

0.62

20.

117

0.11

20.

126

0.40

30.

479

0.00

30.

085

0.02

7

Not

e:T

his

tabl

ere

port

sth

eef

fect

ofa

stan

dard

devi

atio

nin

crea

sein

test

-sco

rean

dle

arni

ng-s

kills

elem

enta

rysc

hool

teac

her

valu

e-ad

ded

(see

Tabl

e4)

onst

uden

ts’

high

scho

olou

tcom

es.

Spec

ifica

lly,

each

colu

mn

ofth

eta

ble

repo

rts

the

coef

ficie

nts

onth

ete

st-s

core

and

lear

ning

-ski

llsno

rmal

ized

mea

sure

sof

teac

herv

alue

-add

edfr

oman

OL

Sre

gres

sion

ofth

est

uden

ts’h

igh

scho

olou

tcom

eon

the

two

mea

sure

sof

teac

herv

alue

-add

edfo

rave

rage

dfo

reac

hst

uden

ts’t

each

ers

from

grad

es4-

11,a

long

with

the

cont

rols

desc

ribe

din

sect

ion

4.1

mea

sure

din

grad

es3

and

4.T

heco

ntro

lsin

clud

ela

gsof

acu

bic

poly

nom

ialo

fthe

stud

ent’s

mat

hte

stsc

ore,

Eng

lish

test

scor

e,G

PA,a

ndef

fort

GPA

;acu

bic

poly

nom

ials

ofla

gged

clas

san

dgr

ade

leve

lmea

nsof

each

ofth

ose

vari

able

s;an

dcu

rren

tEng

lish

lear

ners

tatu

san

dla

gged

log

days

abse

nt,s

uspe

nsio

ns,a

ndhe

ldba

ckfo

rthe

indi

vidu

al,t

hecl

ass

aver

age,

and

the

grad

eav

erag

e.A

cont

rolf

orcl

ass

size

isin

clud

ed.S

tude

nts

are

incl

uded

ifw

eob

serv

eth

eva

lue-

adde

dsc

ores

ofth

eirt

each

ers

fora

tlea

st7

ofth

e8

year

sth

eysp

end

ingr

ades

4-11

.Sta

ndar

der

rors

clus

tere

dat

the

mod

alhi

ghsc

hool

leve

lare

repo

rted

inpa

rent

hese

s.**

*p

<0.

01, *

*p

<0.

05,a

nd*

p<

0.10

.

99

Tabl

eA

.32:

Rel

atio

nshi

pbe

twee

nE

lem

enta

rySu

bjec

tGPA

and

Hig

hSc

hool

Out

com

es

Pool

edG

rade

s3-

5G

PAL

AU

SD

Dro

pout

Took

SAT

SAT

Scor

eG

PAE

ffor

tGPA

Coo

pera

tion

GPA

Mat

h

CA

HSE

E

Eng

lish

CA

HSE

E

Day

s

Susp

ende

d

Log

Abs

ence

sH

eld

Bac

k

Mat

hG

PA-0

.038

***

0.05

4***

56.9

02**

*0.

156*

**0.

075*

**0.

060*

**0.

337*

**0.

096*

**-0

.010

***

-0.1

05**

*-0

.046

***

(0.0

03)

(0.0

04)

(2.9

48)

(0.0

06)

(0.0

04)

(0.0

03)

(0.0

07)

(0.0

05)

(0.0

03)

(0.0

06)

(0.0

04)

Rea

ding

GPA

0.00

40.

011*

*53

.384

***

-0.0

17**

*-0

.015

***

-0.0

09**

*0.

019*

**0.

169*

**0.

012*

**-0

.010

0.01

0***

(0.0

03)

(0.0

05)

(2.6

35)

(0.0

05)

(0.0

04)

(0.0

03)

(0.0

07)

(0.0

07)

(0.0

03)

(0.0

07)

(0.0

03)

Wri

ting

GPA

-0.0

29**

*0.

057*

**-0

.449

0.15

1***

0.08

2***

0.05

9***

0.02

2***

0.10

7***

-0.0

19**

*-0

.016

**-0

.038

***

(0.0

04)

(0.0

05)

(2.1

74)

(0.0

06)

(0.0

03)

(0.0

03)

(0.0

06)

(0.0

06)

(0.0

04)

(0.0

07)

(0.0

03)

Lis

teni

ngG

PA-0

.057

***

0.06

4***

-11.

538*

**0.

210*

**0.

131*

**0.

134*

**0.

048*

**0.

025*

**-0

.070

***

-0.0

69**

*-0

.050

***

(0.0

03)

(0.0

04)

(2.2

65)

(0.0

06)

(0.0

03)

(0.0

03)

(0.0

06)

(0.0

05)

(0.0

05)

(0.0

07)

(0.0

04)

Spea

king

GPA

0.02

4***

-0.0

37**

*0.

697

-0.1

30**

*-0

.081

***

-0.0

88**

*-0

.046

***

-0.0

22**

*0.

040*

**0.

063*

**0.

026*

**

(0.0

03)

(0.0

05)

(2.4

76)

(0.0

08)

(0.0

05)

(0.0

04)

(0.0

07)

(0.0

06)

(0.0

04)

(0.0

07)

(0.0

04)

His

tory

/Soc

ialG

PA-0

.016

***

0.01

8***

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330.

070*

**0.

040*

**0.

031*

**-0

.019

**0.

001

-0.0

13**

*-0

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***

-0.0

22**

*

(0.0

04)

(0.0

05)

(2.2

94)

(0.0

07)

(0.0

04)

(0.0

04)

(0.0

07)

(0.0

06)

(0.0

04)

(0.0

07)

(0.0

04)

Scie

nce

GPA

-0.0

010.

007

21.4

26**

*0.

019*

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008*

0.01

0***

0.02

0***

0.03

8***

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09**

-0.0

30**

*-0

.008

**

(0.0

04)

(0.0

05)

(2.6

53)

(0.0

06)

(0.0

04)

(0.0

04)

(0.0

07)

(0.0

05)

(0.0

04)

(0.0

05)

(0.0

03)

Hea

lthG

PA0.

008*

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001

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0.00

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011*

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006

0.00

3-0

.004

0.00

70.

004

(0.0

04)

(0.0

05)

(3.2

88)

(0.0

07)

(0.0

04)

(0.0

04)

(0.0

09)

(0.0

06)

(0.0

05)

(0.0

07)

(0.0

04)

PEG

PA0.

021*

**-0

.032

***

-30.

433*

**-0

.099

***

-0.0

65**

*-0

.071

***

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46**

*-0

.115

***

0.04

2***

-0.0

060.

024*

**

(0.0

03)

(0.0

04)

(2.8

03)

(0.0

06)

(0.0

03)

(0.0

03)

(0.0

06)

(0.0

05)

(0.0

05)

(0.0

07)

(0.0

05)

Art

sG

PA-0

.016

***

0.01

2*-0

.000

0.06

3***

0.03

7***

0.03

7***

-0.0

090.

046*

**-0

.032

***

0.00

4-0

.011

***

(0.0

04)

(0.0

07)

(2.7

27)

(0.0

06)

(0.0

04)

(0.0

04)

(0.0

08)

(0.0

06)

(0.0

04)

(0.0

08)

(0.0

04)

Obs

erva

tions

206,

598

156,

359

93,4

1740

0,53

232

8,03

532

8,03

522

2,91

622

2,15

843

3,59

638

0,61

231

1,58

5

R-s

quar

ed0.

288

0.16

70.

645

0.28

60.

276

0.28

20.

537

0.54

60.

043

0.26

50.

121

Not

e:T

hesa

mpl

ein

clud

esst

uden

tsin

grad

es3-

5w

hoat

tend

edhi

ghsc

hool

inth

eL

AU

SD.T

heun

itof

obse

rvat

ion

isa

stud

ent-

acad

emic

year

.Thi

sta

ble

repo

rts

the

pred

ictiv

eef

fect

ofa

one-

poin

tinc

reas

ein

ast

uden

ts’G

PAin

each

ofth

e10

elem

enta

rysc

hool

subj

ects

onst

uden

ts’h

igh

scho

olou

tcom

es.S

peci

fical

ly,e

ach

colu

mn

ofth

eta

ble

repo

rts

the

coef

ficie

nts

onea

chof

the

stud

ents

’10

subj

ectG

PAs

from

anO

LS

regr

essi

onof

the

stud

ents

’hi

ghsc

hool

outc

ome

onst

uden

ts’

10su

bjec

tGPA

sin

grad

es3,

4or

5.

The

base

line

cont

rols

incl

ude

lags

ofa

cubi

cpo

lyno

mia

lof

the

stud

ent’s

mat

hte

stsc

ore,

Eng

lish

test

scor

e,G

PA,a

ndef

fort

GPA

;acu

bic

poly

nom

ials

ofla

gged

clas

san

dgr

ade

leve

lmea

nsof

each

ofth

ose

vari

able

s;an

dcu

rren

tEng

lish

lear

ners

tatu

san

dla

gged

log

days

abse

nt,s

uspe

nsio

ns,

and

held

back

for

the

indi

vidu

al,t

hecl

ass

aver

age,

and

the

grad

eav

erag

e.E

ach

ofth

ese

vari

able

sex

cept

Eng

lish

lear

ner

stat

usis

fully

inte

ract

edw

ithgr

ade

fixed

effe

cts

and

aco

ntro

lfor

clas

ssi

zeis

incl

uded

.Sta

ndar

der

rors

clus

tere

dat

the

mod

alhi

ghsc

hool

leve

lare

repo

rted

inpa

rent

hese

s.**

*p

<0.

01, *

*p

<0.

05,a

nd*

p<

0.10

.

100

Tabl

eA

.33:

Eff

ecto

fEle

men

tary

Scho

olTe

ache

rVal

ue-A

dded

onH

igh

Scho

olO

utco

mes

-Clu

ster

ing

atE

lem

enta

rySc

hool

Lev

el

Pool

edG

rade

s3-

5VA

LA

USD

Dro

pout

Took

SAT

SAT

Scor

eG

PAE

ffor

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Coo

pera

tion

GPA

Mat

h

CA

HSE

E

Eng

lish

CA

HSE

E

Day

s

Susp

ende

d

Log

Abs

ence

sH

eld

Bac

k

Test

Scor

eVA

-0.0

02-0

.002

6.23

7***

0.00

20.

003*

0.00

5***

0.02

2***

0.01

6***

0.00

10.

001

0.00

1

(0.0

02)

(0.0

02)

(1.1

52)

(0.0

02)

(0.0

01)

(0.0

01)

(0.0

03)

(0.0

03)

(0.0

01)

(0.0

03)

(0.0

01)

Beh

avio

rVA

-0.0

030.

010*

**1.

955

0.01

3***

0.00

7***

0.00

5**

0.01

3**

0.00

4-0

.003

*-0

.016

***

-0.0

06**

(0.0

02)

(0.0

03)

(2.1

48)

(0.0

04)

(0.0

02)

(0.0

02)

(0.0

05)

(0.0

04)

(0.0

02)

(0.0

05)

(0.0

02)

Lea

rnin

gSk

ills

VA-0

.000

-0.0

01-0

.547

-0.0

02-0

.003

-0.0

03-0

.005

0.00

1-0

.002

0.00

20.

001

(0.0

02)

(0.0

03)

(1.4

85)

(0.0

04)

(0.0

02)

(0.0

02)

(0.0

04)

(0.0

04)

(0.0

02)

(0.0

05)

(0.0

02)

Obs

erva

tions

135,

786

102,

517

60,6

9429

3,02

823

3,07

823

3,07

815

2,34

515

1,82

031

6,12

327

7,33

322

1,75

7

R-s

quar

ed0.

293

0.14

50.

617

0.24

40.

234

0.23

90.

500

0.51

20.

040

0.26

70.

108

Not

e:T

hesa

mpl

ein

clud

esst

uden

tsin

grad

es3-

5w

hoat

tend

edhi

ghsc

hool

inth

eL

AU

SD.T

heun

itof

obse

rvat

ion

isa

stud

ent-

acad

emic

year

.Thi

sta

ble

repo

rts

the

effe

ctof

ast

anda

rdde

viat

ion

incr

ease

inth

eth

ree

mea

sure

sof

elem

enta

rysc

hool

teac

herv

alue

-add

ed(s

eeTa

ble

4)on

stud

ents

’hig

hsc

hool

outc

omes

.Spe

cific

ally

,eac

hco

lum

nof

the

tabl

ere

port

sth

eco

effic

ient

son

each

ofth

eth

ree

norm

aliz

edm

easu

res

ofte

ache

rva

lue-

adde

dfr

oman

OL

Sre

gres

sion

ofth

est

uden

ts’

high

scho

olou

tcom

eon

the

thre

em

easu

res

ofte

ache

rva

lue-

adde

dfo

rth

est

uden

ts’

teac

hers

ingr

ades

3,4,

or5,

alon

gw

ithth

eba

selin

eco

ntro

lsde

scri

bed

inse

ctio

n4.

1.T

heba

selin

eco

ntro

lsin

clud

ela

gsof

acu

bic

poly

nom

ialo

fthe

stud

ent’s

mat

hte

stsc

ore,

Eng

lish

test

scor

e,G

PA,a

ndef

fort

GPA

;acu

bic

poly

nom

ials

ofla

gged

clas

san

dgr

ade

leve

lmea

nsof

each

ofth

ose

vari

able

s;an

dcu

rren

tEng

lish

lear

ners

tatu

san

dla

gged

log

days

abse

nt,s

uspe

nsio

ns,a

ndhe

ldba

ckfo

rthe

indi

vidu

al,t

hecl

ass

aver

age,

and

the

grad

eav

erag

e.E

ach

ofth

ese

vari

able

sex

cept

Eng

lish

lear

ners

tatu

sis

fully

inte

ract

edw

ithgr

ade

fixed

effe

cts

and

aco

ntro

lfor

clas

ssi

zeis

incl

uded

.Sta

ndar

der

rors

clus

tere

dat

the

elem

enta

rysc

hool

leve

lare

repo

rted

inpa

rent

hese

s.**

*p

<0.

01, *

*p

<0.

05,a

nd*

p<

0.10

.

101

Tabl

eA

.34:

Eff

ecto

fEle

men

tary

Scho

olTe

ache

rVal

ue-A

dded

onH

igh

Scho

olO

utco

mes

-Clu

ster

ing

atth

eE

lem

enta

rySc

hool

Coh

ort

Lev

el

Pool

edG

rade

s3-

5VA

LA

USD

Dro

pout

Took

SAT

SAT

Scor

eG

PAE

ffor

tGPA

Coo

pera

tion

GPA

Mat

h

CA

HSE

E

Eng

lish

CA

HSE

E

Day

s

Susp

ende

d

Log

Abs

ence

sH

eld

Bac

k

Test

Scor

eVA

-0.0

02-0

.002

6.23

7***

0.00

20.

003*

*0.

005*

**0.

022*

**0.

016*

**0.

001

0.00

10.

001

(0.0

02)

(0.0

02)

(1.0

23)

(0.0

02)

(0.0

01)

(0.0

01)

(0.0

02)

(0.0

02)

(0.0

01)

(0.0

03)

(0.0

01)

Beh

avio

rVA

-0.0

030.

010*

**1.

955

0.01

3***

0.00

7***

0.00

5***

0.01

3***

0.00

4-0

.003

*-0

.016

***

-0.0

06**

*

(0.0

03)

(0.0

03)

(1.4

94)

(0.0

03)

(0.0

02)

(0.0

01)

(0.0

04)

(0.0

03)

(0.0

01)

(0.0

03)

(0.0

02)

Lea

rnin

gSk

ills

VA-0

.000

-0.0

01-0

.547

-0.0

02-0

.003

*-0

.003

**-0

.005

*0.

001

-0.0

020.

002

0.00

1

(0.0

03)

(0.0

02)

(1.1

73)

(0.0

03)

(0.0

02)

(0.0

01)

(0.0

03)

(0.0

03)

(0.0

01)

(0.0

03)

(0.0

02)

Obs

erva

tions

135,

786

102,

517

60,6

9429

3,02

823

3,07

823

3,07

815

2,34

515

1,82

031

6,12

327

7,33

322

1,75

7

R-s

quar

ed0.

293

0.14

50.

617

0.24

40.

234

0.23

90.

500

0.51

20.

040

0.26

70.

108

Not

e:T

hesa

mpl

ein

clud

esst

uden

tsin

grad

es3-

5w

hoat

tend

edhi

ghsc

hool

inth

eL

AU

SD.T

heun

itof

obse

rvat

ion

isa

stud

ent-

acad

emic

year

.Thi

sta

ble

repo

rts

the

effe

ctof

ast

anda

rdde

viat

ion

incr

ease

inth

eth

ree

mea

sure

sof

elem

enta

rysc

hool

teac

herv

alue

-add

ed(s

eeTa

ble

4)on

stud

ents

’hig

hsc

hool

outc

omes

.Spe

cific

ally

,eac

hco

lum

nof

the

tabl

ere

port

sth

eco

effic

ient

son

each

ofth

eth

ree

norm

aliz

edm

easu

res

ofte

ache

rva

lue-

adde

dfr

oman

OL

Sre

gres

sion

ofth

est

uden

ts’

high

scho

olou

tcom

eon

the

thre

em

easu

res

ofte

ache

rva

lue-

adde

dfo

rth

est

uden

ts’

teac

hers

ingr

ades

3,4,

or5,

alon

gw

ithth

eba

selin

eco

ntro

lsde

scri

bed

inse

ctio

n4.

1.T

heba

selin

eco

ntro

lsin

clud

ela

gsof

acu

bic

poly

nom

ialo

fthe

stud

ent’s

mat

hte

stsc

ore,

Eng

lish

test

scor

e,G

PA,a

ndef

fort

GPA

;acu

bic

poly

nom

ials

ofla

gged

clas

san

dgr

ade

leve

lmea

nsof

each

ofth

ose

vari

able

s;an

dcu

rren

tEng

lish

lear

ners

tatu

san

dla

gged

log

days

abse

nt,s

uspe

nsio

ns,a

ndhe

ldba

ckfo

rthe

indi

vidu

al,t

hecl

ass

aver

age,

and

the

grad

eav

erag

e.E

ach

ofth

ese

vari

able

sex

cept

Eng

lish

lear

ners

tatu

sis

fully

inte

ract

edw

ithgr

ade

fixed

effe

cts

and

aco

ntro

lfor

clas

ssi

zeis

incl

uded

.Sta

ndar

der

rors

clus

tere

dat

the

leve

loft

heel

emen

tary

scho

ol-c

ohor

t.ar

ere

port

edin

pare

nthe

ses.

***

p<

0.01

, **

p<

0.05

,and

*p

<0.

10.

102

Tabl

eA

.35:

Eff

ecto

fEle

men

tary

Scho

olTe

ache

rVal

ue-A

dded

onH

igh

Scho

olO

utco

mes

-Mon

oton

icC

ontr

ols

Pool

edG

rade

s3-

5VA

LA

USD

Dro

pout

Took

SAT

SAT

Scor

eG

PAE

ffor

tGPA

Coo

pera

tion

GPA

Mat

h

CA

HSE

E

Eng

lish

CA

HSE

E

Day

s

Susp

ende

d

Log

Abs

ence

sH

eld

Bac

k

Test

Scor

eVA

-0.0

02-0

.002

5.73

0***

0.00

10.

002

0.00

5***

0.02

1***

0.01

6***

0.00

10.

001

0.00

1

(0.0

02)

(0.0

02)

(1.3

65)

(0.0

03)

(0.0

01)

(0.0

01)

(0.0

03)

(0.0

03)

(0.0

01)

(0.0

03)

(0.0

01)

Beh

avio

rVA

-0.0

030.

010*

**2.

346

0.01

4***

0.00

7***

0.00

5*0.

013*

*0.

004

-0.0

03-0

.016

***

-0.0

06**

(0.0

03)

(0.0

03)

(2.1

25)

(0.0

04)

(0.0

03)

(0.0

02)

(0.0

06)

(0.0

05)

(0.0

02)

(0.0

05)

(0.0

03)

Lea

rnin

gSk

ills

VA-0

.000

-0.0

02-1

.753

-0.0

04-0

.004

-0.0

03-0

.006

-0.0

00-0

.002

0.00

20.

002

(0.0

02)

(0.0

03)

(1.9

90)

(0.0

05)

(0.0

03)

(0.0

03)

(0.0

05)

(0.0

04)

(0.0

02)

(0.0

06)

(0.0

03)

Obs

erva

tions

135,

786

102,

517

60,6

9429

3,02

123

3,07

823

3,07

815

2,34

515

1,82

031

6,11

627

7,33

122

1,75

7

R-s

quar

ed0.

290

0.14

00.

599

0.23

70.

227

0.23

40.

489

0.50

30.

038

0.26

60.

104

Not

e:T

hesa

mpl

ein

clud

esst

uden

tsin

grad

es3-

5w

hoat

tend

edhi

ghsc

hool

inth

eL

AU

SD.T

heun

itof

obse

rvat

ion

isa

stud

ent-

acad

emic

year

.Thi

sta

ble

repo

rts

the

effe

ctof

ast

anda

rdde

viat

ion

incr

ease

inth

eth

ree

mea

sure

sof

elem

enta

rysc

hool

teac

herv

alue

-add

ed(s

eeTa

ble

4)on

stud

ents

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hsc

hool

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omes

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cific

ally

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hco

lum

nof

the

tabl

ere

port

sth

eco

effic

ient

son

each

ofth

eth

ree

norm

aliz

edm

easu

res

ofte

ache

rva

lue-

adde

dfr

oman

OL

Sre

gres

sion

ofth

est

uden

ts’

high

scho

olou

tcom

eon

the

thre

em

easu

res

ofte

ache

rva

lue-

adde

dfo

rth

est

uden

ts’

teac

hers

ingr

ades

3,4,

or5,

alon

gw

ithth

eba

selin

eco

ntro

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bed

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ctio

n4.

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cept

allc

ontr

ols

ente

rth

efu

nctio

nlin

earl

y(n

one

are

cubi

cpo

lyno

mia

ls).

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base

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incl

ude

lags

ofa

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mat

hte

stsc

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lish

test

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held

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lagg

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and

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ele

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mea

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each

ofth

ose

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atus

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ach

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lish

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ner

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fully

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aco

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zeis

incl

uded

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anda

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rors

clus

tere

dat

the

mod

alhi

ghsc

hool

leve

lare

repo

rted

inpa

rent

hese

s.**

*p

<0.

01, *

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05,a

nd*

p<

0.10

.

103

Tabl

eA

.36:

Eff

ecto

fEle

men

tary

Scho

olTe

ache

rVal

ue-A

dded

onH

igh

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eG

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ffor

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pera

tion

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Mat

h

CA

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E

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lish

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E

Day

s

Susp

ende

d

Log

Abs

ence

sH

eld

Bac

k

Test

Scor

eVA

-0.0

02-0

.001

6.08

6***

0.00

30.

003*

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0.01

7***

0.00

10.

000

0.00

1

(0.0

02)

(0.0

02)

(1.2

49)

(0.0

02)

(0.0

01)

(0.0

01)

(0.0

03)

(0.0

02)

(0.0

01)

(0.0

03)

(0.0

01)

Beh

avio

rVA

-0.0

020.

009*

**1.

611

0.01

3***

0.00

7***

0.00

5**

0.01

2**

0.00

3-0

.002

-0.0

16**

*-0

.006

**

(0.0

02)

(0.0

03)

(1.9

61)

(0.0

04)

(0.0

03)

(0.0

02)

(0.0

05)

(0.0

05)

(0.0

02)

(0.0

05)

(0.0

03)

Lea

rnin

gSk

ills

VA-0

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10.

001

(0.0

02)

(0.0

03)

(1.7

63)

(0.0

05)

(0.0

03)

(0.0

03)

(0.0

05)

(0.0

04)

(0.0

02)

(0.0

06)

(0.0

02)

Obs

erva

tions

135,

786

102,

517

60,6

9429

3,02

123

3,07

823

3,07

815

2,34

515

1,82

031

6,11

627

7,33

122

1,75

7

R-s

quar

ed0.

295

0.14

60.

617

0.24

50.

235

0.24

00.

501

0.51

20.

040

0.26

80.

109

Not

e:T

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clud

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uden

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grad

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5w

hoat

tend

edhi

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rvat

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isa

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emic

year

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sta

ble

repo

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the

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ctof

ast

anda

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ion

incr

ease

inth

eth

ree

mea

sure

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elem

enta

rysc

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herv

alue

-add

ed(s

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ble

4)on

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ents

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omes

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cific

ally

,eac

hco

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nof

the

tabl

ere

port

sth

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effic

ient

son

each

ofth

eth

ree

norm

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easu

res

ofte

ache

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oman

OL

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gres

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est

uden

ts’

high

scho

olou

tcom

eon

the

thre

em

easu

res

ofte

ache

rva

lue-

adde

dfo

rth

est

uden

ts’

teac

hers

ingr

ades

3,4,

or5,

alon

gw

ithth

eba

selin

eco

ntro

lsde

scri

bed

inse

ctio

n4.

1ex

cept

allc

ontr

olva

riab

les

(exc

eptd

umm

yva

riab

les)

ente

rthe

func

tion

ascu

bic

poly

nom

ials

.The

base

line

cont

rols

incl

ude

lags

ofa

thir

d-de

gree

poly

nom

ialo

fth

est

uden

t’sm

ath

test

scor

e,E

nglis

hte

stsc

ore,

GPA

,log

days

abse

nt,a

nin

dica

tor

for

susp

ensi

ons,

anin

dica

tor

for

bein

ghe

ldba

ck,a

ndef

fort

GPA

;thi

rd-d

egre

epo

lyno

mia

lsof

lagg

edcl

ass

and

grad

ele

velm

eans

ofea

chof

thos

eva

riab

les;

and

Eng

lish

lear

ners

tatu

sfo

rthe

indi

vidu

al,c

lass

,and

grad

e.E

ach

ofth

ese

vari

able

sex

cept

Eng

lish

lear

ners

tatu

sis

fully

inte

ract

edw

ithgr

ade

fixed

effe

cts

and

ath

ird-

degr

eepo

lyno

mia

lcla

sssi

zeis

incl

uded

.Sta

ndar

der

rors

clus

tere

dat

the

mod

alhi

ghsc

hool

leve

lare

repo

rted

inpa

rent

hese

s.**

*p

<0.

01, *

*p

<0.

05,a

nd*

p<

0.10

.

104