THE IMPACT OF SCHOOL RESTARTS ON TEACHER QUALITY AND RACE IN

NEW ORLEANS

AN HONORS THESIS

SUBMITTED ON THE 25 DAY OF APRIL, 2016

TO THE DEPARTMENT OF ECONOMICS

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

OF THE HONORS PROGRAM

OF NEWCOMB TULANE COLLEGE

TULANE UNIVERSITY

FOR THE DEGREE OF

BACHELOR OF SCIENCES

WITH HONORS IN ECONOMICS

BY

___________________________ Gabriella Runnels

APPROVED: ________________________ Jane Lincove Director of Thesis

________________________ Paula Arce-Trigatti Second Reader

________________________ Michele Adams Third Reader

This page is intentionally blank.

Gabriella Runnels. The impact of school restarts on teacher quality and race in New

Orleans.

(Jane Lincove, Economics)

Most school restarts in New Orleans take the form of charter takeovers.

Research on the effects of charter takeovers shows somewhat positive impacts on

student achievement, although the mechanism of this influence is unclear. Economic

research suggests that teachers are the most important input in the production of

education. Thus, if charters are replacing some or all of the teachers in the schools

they take over, we may expect that this impact on achievement is due, at least in part,

to the new teachers the students in restarted schools are experiencing. This paper

examines whether and how teachers change in New Orleans schools following a

restart. Federal legislation requires public schools in the United States to meet certain

standards of curriculum content and student performance. A school restart, one

strategy for relaunching a school that fails to meet these standards, occurs when an

outside party, such as a charter management organization (CMO), takes over the

operation of the school from the local school district. This study focuses on restarts in

New Orleans, most of which are charter takeovers of low-performing traditional

public schools or other charter schools.

This paper seeks to determine how teachers in restarted schools change, and

future research can determine whether the observed change in teachers is responsible

for any impact on student achievement. I use a difference-in-differences approach and

find that, following a restart, the proportion of both uncertified teachers and teachers

with no teaching experience increases. Furthermore, the percent of both teachers who

are black and teachers who attended college in Louisiana decreases. There is no

significant impact on the proportion of teachers with advanced degrees.

ii

Acknowledgements

Thank you to Dr. Jane Lincove, Dr. Paula Arce-Trigatti, and Dr. Michele Adams

for their crucial contributions to this project. Each committee member has dedicated

significant time and provided valuable guidance in support of this paper. I would like

to express particular gratitude for Dr. Paula Arce-Trigatti, who has worked with me

each week for over a year to brainstorm, shape, and execute this project from start to

finish. I would also like to thank the Education Research Alliance for New Orleans

(ERA) for providing critical resources for this study. This research was supported by

Tulane Economics.

iii

Table of Contents

List of Tables...................................................................................................................................v

List of Figures................................................................................................................................vi

Introduction....................................................................................................................................1

Background and Context.............................................................................................................4

History and Policy............................................................................................................4

Controversy Nationwide.................................................................................................6

Controversy in New Orleans..........................................................................................7

Literature.........................................................................................................................................8

Impacts of Takeovers on Student Achievement.........................................................8

Teacher Characteristics.................................................................................................11

Data................................................................................................................................................15

Variable and Treatment Definitions...........................................................................15

Treatment Response Perspective................................................................................18

Theory and Methods...................................................................................................................19

Conceptual Framework.................................................................................................19

Empirical Methods.........................................................................................................21

Results............................................................................................................................................23

Difference-in-Differences.............................................................................................23

Event Study......................................................................................................................24

Robustness Checks.........................................................................................................25

Conclusion....................................................................................................................................26

References.....................................................................................................................................29

Tables and Figures.......................................................................................................................36

s

iv

List of Tables

Table 1. Average teacher characteristics by school, New Orleans and Louisiana, 2007-2008 and 2013-2014 school years

Table 2. Average school and student characteristics, New Orleans and Louisiana, 2007-2008 and 2013-2014 school years

Table 3. Average teacher characteristics by school, control and treatment, New Orleans, 2007-2008 and 2013-2014 school years

Table 4. Average school characteristics, control and treatment, New Orleans, 2007-2008 and 2013-2014 school years

Table 5. Average teacher characteristics by school, pre- and post-treatment and control, New Orleans

Table 6. Average school characteristics, treatment, pre and post, New Orleans

Table 7. Difference-in-differences results

Table 8. Event study results

v

List of Figures

Graph 1. Average percent black per year, New Orleans

Graph 2. Average percent instate university per year, New Orleans

Graph 3. Average percent uncertified per year, New Orleans

Graph 4. Average percent no experience per year, New Orleans

Graph 5. Average percent with advanced degree per year, New Orleans

Graph 6. Event study results, percent black

Graph 7. Event study results, percent instate

Graph 8. Event study results, percent uncertified

Graph 9. Event study results, percent with no experience

Graph 10. Event study results, percent with advanced degree

vi

1 Introduction

Throughout the United States, public schools are required to meet certain

standards of curriculum content and student performance, and if they fail to meet

these standards, they face being shut down or “redone” in some way. Federal funding

legislation puts forth four possible policies for relaunching underperforming schools:

turnarounds, transformations, closures, and restarts (Ruble 2015). Turnarounds and

transformations are less severe courses of action in which some school staff,

administrators, and policies may be altered, but students are allowed to stay in their

same schools. A closure occurs when a school ceases to operate, while a restart is any

school whose operation is taken over by an outside party, such as a charter

management organization (CMO), and whose students may remain enrolled in the

school. While research on charter takeovers shows a positive impact on student test

scores, the precise mechanism of this effect has not been established (Imberman,

2007; Ruble, 2015). However, because teachers are the biggest determinant of student

outcomes, it would be reasonable to expect that a change in teachers is at least

partially responsible for this impact.

This study seeks to determine whether and how teachers change in schools

following a restart. I will examine the extent to which school-level teacher

characteristics, including race and measures of teacher quality, change following a

takeover. To the best of my knowledge, there is no evidence on how charter takeovers

change teacher characteristics within schools. Discovering how school-level teacher

characteristics change following a restart could allow researchers to determine the

role that teachers play in charter takeovers’ impact on student achievement, especially

since states often turn underperforming schools over to CMOs in order to improve

educational outcomes for students.

1

My study will focus on restarts in New Orleans, the majority of which are

charter takeovers of low-performing traditional public schools or other charter

schools. Following the impact of Hurricane Katrina in 2005, Louisiana seized most of

New Orleans’ public schools and placed them in the state-managed RSD. At first, the

RSD directly ran some schools and contracted out others to CMOs. As of the

2014-2015 academic year, however, all schools in the RSD are charter schools (Sims &

Vaughan, 2014). Conducting this study in New Orleans will be especially interesting

given the city’s unique relationship with charters. New Orleans leads the nation in its

use of charter schools, and this reliance on charters to provide most of the city’s public

education has caused significant controversy. Additionally, this is an important

opportunity to observe what happens when several restarts occur in the same city

around the same time: while many urban centers have likely experienced at least a few

school restarts, New Orleans has seen 26 restarts over the last ten years. For other

urban areas considering restarts as an improvement strategy, the findings from this

study should help inform policymakers how this may play out when taken to scale.

In theory, privatizing education could benefit society by allowing for the

creation of schools with more diverse specializations and different kinds of school

missions than traditional public schools can have (Friedman, 1962). Charters, which

are publicly funded but privately run, are a form of education privatization, and in this

way, converting traditional public schools into charter schools could increase options

for students and parents. Furthermore, charter takeovers could increase the efficiency

of education production as charters compete for contracts from the government

(Friedman, 1962; Sawicky, 1996). Such competition could result in charters being

higher quality and lower cost than traditional public schools, which do not compete

for funding in the same way.

2

The theoretical framework I use for this research is the education production

function (EPF). Rice & Schwartz (2008) explain that research on the production of

education uses a similar framework to other production functions, treating schools,

teachers, peers, et cetera, as the inputs to education and student performance,

primarily measured by test scores, as its output. Measuring the production of

education is complicated. Many outcomes of education are difficult to define in

quantitative terms, such as leadership abilities or critical thinking skills. Most

researchers thus study test scores as the output of the EPF, likely because they are easy

to quantify. In this paper, I will examine teachers as an “input” to education

production; therefore, any observed effect on teachers can be viewed as an effect on

the largest input to the production of student achievement. Future research could then

determine whether the effect of restarts on student achievement is at all due to the

observed change in teachers.

To determine the impact of a school restart on the teacher composition of a

school, I employ a difference-in-differences estimation strategy using school-level data

from the Louisiana Department of Education (LDOE). The first difference accounts for

changes over time, while the second difference accounts for underlying, time-invariant

differences between schools that were restarted and those that never experienced a

restart. I find that following a school restart, the average proportion of teachers who

are black or who attended college in Louisiana decreases and the proportion who are

uncertified or who have no teaching experience increases. There is no significant effect

on the average percent of teachers who hold an advanced degree.

The remainder of this paper is organized as follows: Section 2 describes the

background and context for school restarts in general and specifically in New Orleans.

Section 3 discusses the previous literature on school restarts and teachers, and Section

3

4 describes the data used for this study. Section 5 explains the empirical methods

employed, and Section 6 discusses the results. Finally, Section 7 presents the

conclusions to this paper.

2 Background and Context

2.1 History and Policy

Following the publication of A Nation at Risk, the landmark 1983 report on the

state of education in the United States, American education policies have focused on

standards-based reforms, which set criteria for the content areas of instruction and

the performance level of the students that schools are required to meet. In 2001,

Congress passed the No Child Left Behind (NCLB) Act, which aims to increase the

academic performance of the nation’s public schools by requiring states to institute

standards-based accountability measures and taking action against those schools that

fail to achieve them (Springer, 2008). The reasoning behind these reforms is that

creating incentives for school administrators and teachers to meet performance goals

will result in improved student achievement and other outcomes (Sims, 2013).

Before the mid-1990s, takeovers were primarily motivated by financial or

management issues, but in recent years, most takeovers have occurred as a result of

schools failing to meet accountability standards for academics set by the state

(Oluwole & Green, 2009). When the state takes over an underperforming school, it

can either run the school directly or contract the school out to another manager. In

compliance with NCLB, the Louisiana legislature passed an amendment in 2003

granting the government the power to shut down or take over the operations of

“failing” schools. A school is considered failing when it does not meet the minimum

accountability standards set by the state, which are based on metrics including student

test scores and graduation rates (Burns, 2010). When the state takes over a failing

4

school, it can either run the school directly, or it can contract the school out to a CMO

or other such operator. In Louisiana, the Recovery School District (RSD) is the

administrative body tasked with taking over and improving sub-standard public

schools. After Hurricane Katrina hit New Orleans in 2005, destroying most of the

city’s school buildings and displacing thousands of students, the legislature turned

over to the RSD more than 100 of New Orleans’ worst-performing schools (American

Youth Policy Forum, 2008). Most of these schools were subsequently contracted out as

charter schools, which are publicly-funded but independently-run.

Beginning in the 2014-2015 academic year, all New Orleans schools taken over

by the RSD have been turned over to CMOs (Sims & Vaughan, 2014). Other cities

across the country, including the notable examples of Detroit, Philadelphia, and

Cleveland, have begun to follow New Orleans’ lead in using charter schools to such an

extent (National Alliance for Public Charter Schools, 2014). Indeed, in 2010,

Education Secretary Arne Duncan stated that Hurricane Katrina was “the best thing

that happened to the education system in New Orleans” (Anderson, 2010). Although

he later apologized for this insensitive remark, the sentiment behind his declaration

was clear: New Orleans school reforms, including the numerous restarts that have

occurred since 2005, appear to work.

2.2 Controversy Nationwide

Not everyone was as certain of the effectiveness of accountability standards

and charter schools as was Secretary Duncan, however. The charter school takeover

system and other standards-based school reform measures have met with significant

controversy from parents, teachers, activists, and other policymakers. In 2015, these

concerns reached the highest levels of education policymakers: on October 24,

Duncan stated that he has had innumerable conversations with educators “who are

5

understandably stressed and concerned about an overemphasis on testing” and that

the Obama administration “will work with states, districts and educators to help solve

[this problem]” (Zernike, 2015).

Controversy over school reforms is not new, and some of the major historical

concerns raised by opponents about takeover policies are relevant today. As soon as

takeover reform strategies started rising in popularity in the 1990s, critics began

speaking out about some of the problems they perceived. Opponents of these reforms

take issue with the idea of predominantly white governing bodies and administrators

preventing racial minorities from controlling their own domains (Green and Carl,

2000). They condemn the biases in most standardized tests used in accountability

metrics, a phenomenon that was studied and confirmed in 2002 by economists Kane

and Staiger, who found that measures of standardized test scores are largely unreliable.

Furthermore, critics claim that takeovers do not address the root of the problem of

inequities in education along racial lines. Indeed, failing schools that are subject to

state takeovers are mostly, if not uniformly, majority students of color and are

generally run by non-white administrators. In contrast, most of the policymakers and

takeover advocates at all stages of the reform history have been white, some of them

the “politicians who resisted providing educational funding for these same

districts” (Green & Carl, 2000, p. 64).

2.3 Controversy in New Orleans

Some non-white community leaders and citizens have also supported takeover

measures and greater state influence over local education in general (Green & Carl,

2000; Burns, 2010). However, when Louisiana districts voted on the takeover

amendment in 2003, jurisdictions in which black residents turned out to vote in large

numbers strongly opposed the policy, whereas majority white areas disproportionately

6

supported it (Burns, 2010). This strong reaction to the takeover policy from black

voters is likely rooted in the racial nature of the structure and implementation of the

reforms. After the hurricane, the Orleans Parish School Board (OPSB) fired all of the

city’s public school teachers, most of whom were black women (Carr, 2015). The U.S.

Supreme Court recently denied the appeal of nearly 7,500 public school employees

who filed a class action lawsuit against the OPSB after the Louisiana Supreme Court

threw out their case (Dreilinger, 2015). To this day, New Orleans schools remain

deeply segregated along lines of race and class, whereby the city’s public schools are

majority black and low-income students, while the private schools have more white

and higher income students. Some New Orleanians blame the rise of charter schools

for continuing these patterns of educational inequality in the city (Institute on Race

and Poverty, 2010). In particular, a common perception among New Orleans residents

is that charter schools are taking over traditional public schools and replacing

established, local black teachers with inexperienced, uncertified, mostly white teachers

who come to New Orleans from out of state through organizations like Teach for

America.

3 Literature

3.1 Impacts of Takeovers on Student Achievement

A “charter” is a contract that grants private organizations the authority to

operate schools using public funds. Most literature on the effectiveness of charter

schools does show generally positive impacts on student achievement compared to

traditional public schools (Hoxby, 2004; Hoxby & Rockoff, 2005; Imberman, 2007;

Abdulkadiroğlu et al., 2009; Hoxby, Murarka, & Kang, 2009; Abdulkadiroğlu et al.,

2010; Angrist, Pathak, & Walters, 2011; Angrist et al., 2011; Tuttle et al., 2013;

7

CREDO, 2013); however, a smaller body of research shows mixed or even negative

effects (Sass, 2004; CREDO, 2009; Zimmer et al., 2009; Gleason et al., 2010). Despite

this bulk of research on the impacts of charter schools, not much evidence exists

specifically on the effects of charter takeovers of low-performing schools. Studies that

do analyze the impacts on student achievement of regular public schools that close

and then reopen under charter management show mixed results (Abdulkadiroğlu et

al., 2014).

A few studies separately examine the effects of converted charter schools and

startup charters (Imberman, 2007; Buddin & Zimmer, 2003; Buddin & Zimmer, 2005).

“Startups” are schools that begin as charters, while “conversions” are schools that are

initially traditional public schools but convert to charters (Imberman, 2007). In this

literature, “conversions” are comparable to charter takeovers or school restarts, while

“startups” would be new charter schools that did not take over the operation of

another existing school. Imberman (2007) uses individual fixed effects strategies to

evaluate the success of charters in an anonymous large urban school district, and he

finds mild evidence of an increase in test scores for converted charters. Although

initial results indicate a positive impact, the effects become mostly insignificant when

gifted and talented magnet schools are taken out of the analysis. Similarly, Buddin &

Zimmer (2003; 2005) do not find significant impacts of startup or converted charters

on achievement. In their studies, Buddin & Zimmer separate their analyses of charter

schools into four categories: conversion charters, startups, charters that use

classroom-based instruction, and those with instruction based outside of the

classroom. They note that most research on charter school performance treats

charters as a homogenous group, obscuring differences in their structures and

effectiveness. They find that startups and conversions were not meaningfully different

8

from each other in terms of impact on test scores (2003), and neither types of charter

school were significantly different from conventional public schools (2003).

Studies that analyze the effects of contracting out low-performing schools on

student achievement show mixed results. In these studies, “contracting out” a school is

comparable to taking over or chartering an existing school. For example, Gill et al.

(2007) analyze Philadelphia’s Diverse Provider Model, a policy that turned over 45

public schools to private managers, and find that these managers neither improve nor

harm student test scores. In contrast, Ruble (2015) shows that students enrolled in

contracted-out schools experienced increases in test scores. Ruble’s work on the

impact of school restarts on student achievement is the paper that most closely

resembles the present study. Her study examines whether and how contracting out

underperforming schools to CMOs in New Orleans affects student test scores. New

Orleans’ especially extensive use of charter schools provides a unique opportunity to

study contracting in education. Ruble evaluates the causal effects of contracting out

low-performing schools, including both regular public schools and failed charter

schools, and finds that test performance improves two years after a charter takeover of

a traditional public school and only one year after a takeover of another charter

school. A potential explanation for the differing results between Ruble’s and Gill et al.’s

studies may be that the Philadelphia model did not have many providers competing

for contracts, whereas Ruble’s study is the first of its kind to analyze the effectiveness

of contracting out education in an environment with high competition.

The general focus of the previously discussed literature is to examine how

school takeovers impact student performance. These studies, for the most part, do not

address the actual mechanism responsible for the observed impact on achievement. In

9

particular, there is no analysis of how teachers, the most important determinant of

student achievement, are impacted by charter takeovers.

3.2 Teacher Characteristics

We may expect that any impact of takeovers on student achievement could be

at least partially due to a change in teachers, as teachers are by far the most important

input to the education production function (Rice & Schwartz, 2008). Most studies

measuring teachers’ impact on student achievement have looked at teachers’ “value-

added,” which represents each teacher’s individual contribution to student test scores;

that is, the “value” that a given teacher “adds” to a student’s education. A wealth of

research has shown that there is significant variation in teacher value-added,

suggesting that some teachers are much more effective than others (Hanushek, Kain,

O’Brien, & Rivkin, 2005; Rivkin, Hanushek, & Kain, 2005; Hanushek, Kain, O’Brien, &

Rivkin, 2005; Chetty, Friedman, & Rockoff, 2014; Nye, Konstantopoulos, & Hedges,

2004; Aaronson, Barrow, & Sander, 2007; Sanders and Rivers, 1996). Existing literature

on the impacts of specific teacher characteristics, however, cannot completely account

for this variation in teacher quality, as most teacher characteristics, including level of

education, years of experience, and type of certification, show little or no effect on a

teacher’s value-added.

While many parents, school officials, and policymakers believe that higher

teacher quality is necessary for improving education, evidence on the effect of

observable teacher traits on student achievement is inconclusive (Rockoff, 2004).

“Observable teacher characteristics” include attributes like years of experience, race,

gender, and subjective evaluations by principals, among others. Aaronson et al. (2007)

study traditional human capital measures of teacher quality and find that “the vast

majority of the total variation in teacher quality is unexplained by observable teacher

10

characteristics” (p. 129). In particular, they find no impact of level of education, type of

certification, or years of teaching experience on teacher fixed effects, although they do

find that female and African American teachers produce somewhat higher test scores

than do male and white teachers. In slight contrast, Boyd et al. (2008) use a value-

added model to estimate the effect of teacher characteristics on achievement and find

that, taken together, increased teaching experience, higher value-added scores, and

being certified had a modestly positive impact on the achievement of students in the

poorest schools.

Studies that look at the impact of individual characteristics on student

achievement show that some traits, such as years of experience, are related to teacher

quality, but most are unrelated or only related in a limited way. Hanushek (1971) finds

no impact on teacher quality of teaching experience, while Rivkin et al. (2005), Kane et

al. (2008), Rockoff (2004), and Nye et al. (2004) all show that experience does matter,

at least within the first few years. Hanushek (1971) and Rivkin (2005) find no link

between a teacher’s level of education, including a master’s degree, and teacher quality.

In their research on teacher certification, Goldhaber and Brewer (1997) find some

evidence that certification matters for students’ mathematics achievement, and Kane

et al. (2008) similarly find that whether or not a teacher is certified does have a small

effect on student performance. Taken together, the literature suggests that while some

observable attributes of teachers may influence students’ achievement, they do not

definitively account for the wide variation in teacher quality.

Economists have studied the impacts of teacher experience, education,

certification, and race on student achievement. While certain of these attributes (or

combinations of them) do have an impact on student performance, these teacher

qualities cannot fully explain the wide variation in teacher effectiveness (Hanushek,

11

1971; Goldhaber and Brewer, 1997; Rockoff, 2004; Nye et al., 2004; Rivkin, 2005;

Aaronson et al., 2007; Kane et al., 2008). Nevertheless, examining how the

composition of teachers changes in terms of these characteristics could provide

insight into the strategies school leaders are pursuing in restarting underperforming

schools. Furthermore, there is no literature that I know of on the impact of teachers’

geographic background on their effectiveness, but like race or gender, sharing an

identity with students, such as the identity of being from Louisiana, could be

impactful. Thus, determining whether and to what extent the New Orleans teaching

force is becoming populated with fewer local teachers and more out-of-state teachers

could provide another possible explanation for the observed changes in student

performance.

Race and Gender

Few studies of teacher characteristics look at the impact of a teacher’s gender

or race on achievement of students of the same (or different) race or gender, and those

that have analyzed these characteristics have shown mixed results. Ehrenberg et al.

(1995) and Dee (2004) both attempt to answer the question of whether and to what

extent a teacher’s race (and, in Ehrenberg et al.’s case, gender) affect the achievement

of students of the same race (or gender) as the teacher. Furthermore, Ehrenberg et al.

and Dee study the impact of racial matching between teachers and students on

teachers’ subjective evaluations of their students.

Using data from the National Educational Longitudinal Study of 1988,

Ehrenberg et al. (1995) examine the impact of a teacher's race, gender, and ethnicity

(RGE) on the achievement of students from similar and different RGE groups. For

each student group, the authors estimate the gain score equations, which measure the

increase in test scores between the students’ 8th and 10th grade years. Only a few of

12

the coefficients are significant, suggesting that, overall, teachers’ RGE did not play a

very important role in how much students learned. In contrast, Dee (2004) finds that

exposure to a teacher of a student’s own race does have a significant positive impact

on a student’s achievement. Using data from Tennessee’s Project STAR experiment, he

regresses student test scores on student, teacher, and classroom traits. The Project

STAR study randomly paired students and teachers within schools, which means it is

unlikely that the correlations between racial matching and achievement are simply due

to unobserved student characteristics. His models all include fixed effects for the

grade, entry wave, and school of entry, and he identifies the impact on students of

exposure to an own-race teacher. His results suggest such exposure produces

achievement gains for most student racial groups.

Race is a particularly important teacher characteristic to study in New Orleans,

since it is widely believed by the city’s residents that for schools in general, and for

charter takeovers in particular, the teaching force is becoming gradually more white as

established black teachers are being pushed out of schools to make way for

inexperienced, non-local white teachers from organizations such as Teach for America

and TeachNOLA. Because the existing literature on the impact of a teacher’s race on

student achievement is limited and shows mixed results, a study on teacher race in

New Orleans would be especially interesting in terms of showing how changing the

race of the teachers that students experience affects students’ educational

performance.

In the following sections, I will examine the change in teacher characteristics,

including level of education, years of experience, certification status, race, and others,

that is due to a charter takeover of an underperforming public or charter school.

Research shows that, to some extent, charter takeovers do impact student

13

performance, but the exact mechanism of this influence is unclear. If charters are

replacing some or all of the teachers in the schools they take over, we may expect that

this impact is due, at least in part, to the new teachers that the students are

experiencing. The literature shows that, while teachers have a large effect on student

achievement and this effect varies widely, observable teacher attributes do not

completely explain variation in teacher quality. Researchers still debate the causal

effect of teacher characteristics on student achievement, and data on the teachers in

the unique case of New Orleans could provide new insights to this debate.

Furthermore, economists have not given much attention to the impact of a teacher’s

race on the learning of a student of the same (or different) race, and New Orleans’

charter schools, which have been widely criticized for furthering problems of racial

inequality in the city’s education system, would be an especially interesting

environment in which to study this effect.

4 Data

This study uses school-level data from the Louisiana Department of Education

(LDOE) for the academic years 2007-2008 through 2013-2014. This timeframe allows

for three years of data before and three years of data after most restarts occurred. The

dataset has information on all public schools in Louisiana, but the baseline sample for

this study contains only public schools in Orleans Parish, including schools in the

RSD, schools operated or chartered by the Orleans Parish School Board (OPSB), and

schools chartered by the Louisiana Board of Elementary and Secondary Education

(BESE).

4.1 Variable and Treatment Definitions

This paper attempts to answer the question of whether and to what extent

average teacher characteristics, including race, years of experience, educational

14

degree, certification status, and location of university attended, are changing within

restarted schools. “Race” is defined in terms of the percentage of teachers who are

black (few teachers are a race other than white or black); “years of experience”

represents the percent of teachers who have zero years of teaching experience;

“educational degree” refers to the percent of teachers with an advanced degree,

including a master’s, educational specialist, or doctorate (few teachers have anything

less than a bachelor’s degree); “certification status” refers to the percentage of teachers

who have no teaching certification; and “location of university attended” represents

the percentage of teachers who attended college in the state of Louisiana.

Federal policy describes turnarounds, transformations, closures, and restarts as

the four possible strategies for redoing underperforming schools. In New Orleans, a

school is at risk of being relaunched if its school performance score (SPS) is

consistently near or below the threshold for failure. The SPS is measured each year

and primarily reflects student achievement on standardized tests (Cowen Institute,

2015). The failing school SPS cutoff was 60 out of 200 possible points in 2008 and was

raised to 75 in 2012 (Ruble 2015). In 2013, the SPS scale was readjusted to a 150-point

range, and the cutoff for failure was changed to 50 points (The Lens, n.d.). In Table 2,

which shows the average school and student characteristics of schools in New Orleans

and Louisiana for the 2007-2008 and 2013-2014 school years, the average SPS for a

New Orleans public school school in the 2007-2008 academic year was 52 out of 200,

while the average SPS was 70.5 out of 150 in 2013-2014. Nearly half of all New Orleans

public schools experienced either a restart or a closure between 2007 and 2014.

This study focuses on restarts. The treatment group in this paper is comprised

of schools that are restarted between 2007-2008 and 2013-2014, while the control

group is made up of schools that are never redone in that timeframe. Schools that are

15

closed during this timeframe are left out of the sample. This study seeks to determine

the effect of a restart on schools’ teacher compositions, and school closures fall neither

in the treatment group, since they do not experience any years of operation post-

treatment, nor the control group, since they were identified as low-performing and

selected to be redone during that time period.

This final sample contains 90 unique schools, including 64 control schools and

26 treatment schools. In Table 3, which shows average teacher characteristics by

school for the control and treatment schools during the 2007-2008 and 2013-2014

school years, schools in the treatment group are observably different from schools that

were not restarted between 2007 and 2014. In the 2007-2008 school year, the first year

of the observed treatment period, the average proportion of black teachers in control

schools was 55 percent, while it was 64 percent in treatment schools. By the

2013-2014 academic year, the last year in the treatment period, those proportions had

declined for both groups to 48 percent for control schools and 54 percent for

treatment schools. The average proportion of teachers with an advanced degree in

2007-2008 was 32 percent for control schools and 21 percent for treatment schools,

and those proportions both increased to 35 and 27, respectively, in 2013-2014. In

2007-2008, 66 percent of teachers in both treatment and control schools on average

had attended an instate university, while only 53 percent of control teachers and 51

percent of treatment teachers did so by 2013-2014. Average years of teaching

experience for teachers in control schools declined from 11.3 years in 2007-2008 to 9.6

years in 2013-2014, while it declined from 9.4 to 7.5 in treatment schools over that

time period. The average percent of uncertified teachers increased from 17 to 21

percent in control schools and decreased from 27 to 24 percent in treatment schools

over the course of the treatment period. Finally, average wages for teachers in both

16

types of schools decreased between 2007-2008 and 2013-2014, with wages in control

schools higher than those in treatment schools for both years.

4.2 Treatment Response Perspective

A study on how teachers change following a school restart could consider the

question from any of three different points of view: the student level, the teacher level,

or the school level. From the student-level perspective, after a restart occurs, each

student either remains in the restarted school (in which case they likely experience

new teachers) or transfers to another school (in which case they certainly experience

new teachers). In either case, the restart is causing the student to be exposed to

different teacher characteristics than those they previously experienced, which could

impact the student’s educational outcomes. To perform this analysis from the student-

level perspective, a researcher would need to know whether a student stays in the

restarted school or leaves, and if they do leave, where they go. The student-level

analysis is arguably the most important point of view to consider, as the ultimate goal

of most research in this field is to determine the impact of different school treatments

on student outcomes.

Alternatively, this analysis could be performed from the teacher-level

perspective, in which the question of interest would be where teachers go after a

restart. Do they stay at the same school, move to a different school, or leave teaching

altogether? This analysis would require information on individual teacher mobility.

This study, however, will approach the question from the school-level point of view, in

which the central question concerns how and to what extent the composition of

teacher characteristics in a school changes after a school is restarted. My analysis will

not be able to show whether it is the school leaders or the teachers themselves that are

the driving force behind any observable changes in teachers in New Orleans public

17

schools. In other words, this school-level analysis will only show how teacher

composition is changing from the school’s perspective, but it will not explain the

mechanism behind that change. This choice in perspective is largely due to limitations

of the data, but results from this study will still be useful to researchers who do have

access to student-level data. Future analyses that follow students after a restart can use

this research to estimate the average effect on the teacher characteristics that a

student experiences of moving to a charter school from a district school, to a district

school from a charter, to a charter from another charter, or remaining in their same

school after a restart has taken place.

5 Theory and Methods

5.1 Conceptual Framework

Public schools in New Orleans are typically closed or restarted due to low

school performance, which is primarily measured by student test scores. Thus, restarts

are generally intended to improve student outcomes in previously low-achieving

schools. According to Hanushek, Kain and Rivkin (2004), one effect of a restart is the

school quality effect. Using an education production function framework, we might

expect that, in order to improve school quality, decision-makers would replace

teachers from the low-performing schools with new, “higher quality” teachers.

Teachers are the most important input to the education production function and have

been shown to have the largest impact on student test scores. Although literature

analyzing the impact of teacher qualities such as experience, certification, and

education on student achievement shows mixed results at best, we might still expect

decision-makers to choose teachers based on these visible characteristics that are

associated with higher quality teaching.

18

However, New Orleans is a unique case; this city is a popular destination for

organizations like Teach for America, TeachNOLA, and City Year, which typically hire

recent college graduates with little or no experience to teach in underserved

classrooms. Teach for America (TFA) alone has 300 teachers stationed in Orleans,

Jefferson, and St. Bernard parishes and makes up 20 percent of the teaching force of

New Orleans (Teach for America, 2016). While data on the precise locations and

mobility of TFA and similar teachers in New Orleans is not readily available, a

common local narrative is that experienced, established black teachers are being

replaced with inexperienced, non-local, mostly white Teach for America and

TeachNOLA teachers, particularly in schools that are restarted or taken over by

charter schools (Vanacore, 2011).

It seems to be that CMOs in New Orleans have strong working relationships

with these kinds of teaching organizations. Therefore, instead of replacing teachers in

low-performing schools with “higher quality” teachers (teachers with a more advanced

degree, a higher certification status, or more years of experience, for example), New

Orleans schools leaders in restarted schools are likely replacing teachers with mostly

inexperienced college graduates from organizations like Teach for America. In this

analysis, we would thus expect to see an increase in the proportion of teachers who

are white, inexperienced, non-local (measured indirectly by location of university

attended), uncertified, and who have at most a bachelor’s degree in New Orleans

public schools following a restart.

5.2 Empirical Methods

To estimate the effect of restarts on school-level teacher characteristics

including race, years of experience, educational degree, certification status, and

university location, I compare the outcomes of a school that was restarted to those of a

19

school that was never restarted over time. I identify the policy effects from changes in

these outcomes over time and across restarted and non-restarted schools using a

difference-in-differences approach. Specifically, I estimate the following:

, (1)

where the outcome is the percent teacher characteristic given school s and year t.

The model includes school and year fixed effects ( and , respectively), which

measure unobservable characteristics that do not change over time. In this model,

captures time-invariant differences among schools, including whether or not a school

is in the treatment group (that is, whether or not a school is eventually restarted). The

variable represents the effect of anything that impacted all schools in a given year

between 2008 and 2014, such as citywide budget cuts.

I also include a vector of potentially time-varying school characteristics, ,

which is comprised of the number of students in a school, the school’s SPS, whether

the SPS is unreported in a given year (schools typically do not report an SPS in their

first year of operation), and student demographics (student race, gender, and

participation in the free or reduced lunch program). The indicator variable is

the interaction of being in the treatment group and being in a year post-treatment;

that is, indicates that school s is a post-treatment school. Finally,

represents the effect of any omitted variables.

The coefficient of interest is , which captures the average effect of restarts on

several school-level teacher characteristics before and after the restart occurred across

Yst = β0 + β1Restartst +δ s + γ t + Xstβ2 + ε st

Yst

δ s γ t

δ s

γ t

Xst

Restartst

Restartst ε st

β1

20

schools that were restarted relative to those that were not. The identifying assumption

in this case is that, conditional on the school and year fixed effects as well as on the

time-varying school characteristics, the implementation of school restarts is

uncorrelated with other determinants of teacher-level outcomes. In this model, we

cannot assume school observations across years are independent, so I cluster the

standard errors by school.

While represents the average change in school-level teacher characteristics

due to the policy, the difference-in-differences approach from Equation (1) may hide

differential effects that could occur in each year. For this reason, I also estimate the

policy effects using a more flexible event study approach. Specifically, I estimate the

following:

, (2)

where is the percent teacher characteristic, represents the unobservable

differences between schools, represents the unobservable differences across

schools over time, is a vector of time-varying school characteristics, and is the

error term. In Equation (2), indicates that school s was restarted j years from

t. The parameters of interest, , are the coefficients on the event variables. For

example, gives the effect of a restart on the school-level teacher characteristic one

year after the restart was announced.

β1

Yst = β0 + θ jRestartsjtj=−3

3∑ +δ s + γ t + Xstβ2 + ε st

Yst δ s

γ t

Xst ε st

Restartsjt

θ j

θ1

21

This event study approach allows me to test whether treatment schools

(schools that experienced a restart) had, on average, similar trends in outcomes to

those of the control schools (schools that never experienced a restart) during the years

before the treatment had taken place.

6 Results

6.1 Difference-in-Differences

Table 7 compares the results of estimating Equation (1) using two different

definitions of the “pre-treatment” and “post-treatment” periods. Panel A uses the

announcement year (that is, the year in which the restart was announced to occur for

the following year) as the first year in the post-treatment period. Panel B includes the

announcement year in the pre-treatment period and defines the year following the

announcement year (the first year in which the school has been restarted) as the first

year post-treatment. It is reasonable to include the announcement year in the post-

treatment period because we might expect that, in reaction to the news that their

school is going to be restarted the next year, teachers may choose to leave the school.

Thus, the announcement itself can be viewed as part of the treatment.

Both panels produce similar trends in results, but none of the Panel A

coefficients are significant, while all of the Panel B coefficients, with the exception of

advanced degree, are significant at either the 0.01 or 0.05 level. The results from Panel

A show insignificant effects of -0.05 for average percent black, -0.04 for average

percent who attended college in Louisiana, 0.01 for average percent uncertified, and

0.02 for average percent with no teaching experience. The results from Panel B show

the same direction of trends but with larger and significant effects. Estimating the

model with the announcement year included in the pre- rather than post-treatment

period shows a decrease in the average percentage of black teachers of 14 percent and

22

a decrease in the average percentage of teachers who attended an instate university of

17 percent, while the average proportion of uncertified teachers and of teachers with

no experience increased by 8 percent and 14 percent, respectively. Neither panel

shows significant effects on the average percentage of teachers who hold advanced

degrees. Although the outcomes are sensitive to treatment definition, the results from

both panels suggest that among similar schools, the effect of a school restart on

within-school changes is that the teaching populations become proportionally less

black, less local (measured indirectly by location of university attended), more

uncertified, and more inexperienced.

6.2 Event Study

Because we might expect that teachers would respond to the news of a restart

in the announcement year, I estimated Equation (1) first with the announcement year

included in the post-treatment period, then with the announcement year as part of the

pre-treatment period. Although the two estimation strategies produced similar trends

in results, the magnitude and significance of the outcomes were different. To

investigate the possibility that teachers are reacting to the treatment in some way

during the announcement year, I estimated Equation (2), an event study analysis that

assesses the impact of the treatment in each year relative to the control schools and to

a given omitted treatment year. In this case, I omit the year prior to the announcement

year because theoretically, there should be no treatment effects during that year.

Graphs 6 through 10 show that for some characteristics, the announcement

year does have a significant effect, but the effect is often in the opposite direction as

that of the rest of the years post-treatment. Including the announcement year in the

post-treatment period therefore decreases the magnitude and significance of the effect

of the treatment, since the effect of the treatment year alone cancels out some of the

23

effect of the rest of the years. Similarly, including the announcement year in the pre-

treatment period, thereby treating it as though it has no treatment effects, likely

artificially inflates the magnitude and significance of the effect of the post-treatment

period beginning with the first year of the restart. Therefore, the true effects likely fall

somewhere in between the results of Panel A in Table 7 and those of Panel B.

6.3 Robustness Checks

In order to confirm that the results of the event study method are robust to

model specifications, I conducted some robustness checks. Table 8 shows the results

from three different event study models. All three models include clustered standard

errors and student demographics and omit the year prior to the announcement year.

Additionally, all three models group together the effects of three and four years post-

announcement year. Model 1 is the tabled results of estimating Equation (2) using all

schools and all years in the base sample and groups together the effects of three, four,

and five years prior to the announcement year. To show comparisons, Model 2 drops

any schools that have data on five years prior to announcement year and groups

together observations on three and four years prior, while Model 3 drops schools with

either four or five years prior. The results from Model 2 have the same trends for the

announcement year as those from Model 1, but the effects are larger and more

significant. Model 3 produces very similar results to Model 1 in terms of significance

and magnitude for the announcement year. Although the event study method is

sensitive to model specifications, the results of three models taken together suggest

that the announcement year has significant effects on most of the observed teacher

characteristics in the opposite direction as that of the rest of the post-treatment years.

In other words, while the post-treatment period as a whole shows a decrease in the

proportion of teachers who are black or who attended an instate university and an

24

increase in the proportion who have no certification and no years of teaching

experience, the announcement year alone shows increased percent black, increased

percent instate, decreased percent uncertified, and decreased percent inexperienced.

7 Conclusion

Since 2007, nearly half of all New Orleans public schools have experienced

either a restart or a closure as a result of failing to meet statewide standards of student

performance. In New Orleans, most school restarts take the form of a charter takeover

of a traditional public school or other charter school. Although research suggests that

these restarts do have a positive impact student achievement (Imberman, 2007; Ruble,

2015), no research exists, that I know of, on the impact of charter takeovers on

teachers. If charter takeover schools are replacing some or all of the teachers in the

schools they take over, we may expect that this impact on achievement is at least

partially explained by the change in teachers that the students in restarted schools are

experiencing.

This paper uses a difference-in-differences approach to estimate the average

effect of school restarts in New Orleans on within-school teacher characteristics

including race, location of university attended, certification status, teaching

experience, and educational degree. Between 2007 and 2014, restarting schools

resulted in schools having, on average, a smaller proportion of black teachers, a

smaller proportion of teachers who attended college in Louisiana, a larger proportion

of teachers without any certification, and a larger proportion of teachers with zero

years of teaching experience. There was no apparent impact on the average percent of

teachers who hold advanced degrees. These findings are conceptually consistent with

the assumption that, after a restart, decision-makers are bringing in teachers from

organizations like Teach for America to the schools.

25

This study cannot determine whether the change in teachers is a result of

decisions by school leaders (e.g., firing and replacing teachers) or by the teachers

themselves (e.g., voluntarily leaving schools that have been restarted). This analysis

also cannot establish how the students themselves are experiencing any change in

teachers. Future research with access to student-level data can see whether or not

students stay in their same schools once the schools are restarted or, if the students

choose to leave instead, where they go. Then, based on the results of this study, they

can see whether any observed impact of school restarts on student achievement can

be attributed to the change in teachers that those students are experiencing.

Economic theory suggests that charter takeovers could benefit society;

however, the specific ways in which charter takeovers are executed may result in

disadvantageous, or at least controversial, effects. New Orleans has a unique historical

relationship with charter schools, which have especially flourished in the city after

Hurricane Katrina. New Orleans has been a leader in the charter school movement,

and its extensive use of charter schools has caused significant controversy. Charter

takeovers in particular are viewed by many New Orleans residents as detrimental to

racial progress in the city. A common local narrative is that predominantly white

CMOs are taking over the operations of majority black public schools and replacing

longstanding, experienced black teachers with unqualified, mostly white recent college

graduates who have no real ties to the city and rarely teach for longer than a few years.

While this analysis cannot directly confirm those assumptions, the results presented

in this paper suggest that decision-makers in school restarts may be relying on TFA-

type organizations to supply their teachers, which is fundamentally changing the

quality and race of the teaching populations in New Orleans public schools.

26

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8 Tables and Figures

34

35

36

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

-3 -2 -1 0 1 2 3Yearssinceannouncementyear

GRAPH6. RESULTS,PERCENT BLACK

37

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

-3 -2 -1 0 1 2 3Yearssinceannouncementyear

GRAPH7. RESULTS,PERCENT INSTATE

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

-3 -2 -1 0 1 2 3Yearssinceannouncementyear

GRAPH8. RESULTS,PERCENT UNCERTIFIED

38

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

-3 -2 -1 0 1 2 3Yearssinceannouncementyear

GRAPH9. RESULTS,PERCENT NOEXPERIENCE

-0.15

-0.1

-0.05

0

0.05

0.1

-3 -2 -1 0 1 2 3Yearssinceannouncementyear

GRAPH10. RESULTS,PERCENTWITHADVANCEDDEGREE

39

40

41

42

43

44

45