|Martin Haberman, Distinguished Professor Emeritus, UWM
President & CEO
NBER WORKING PAPER SERIES
CAN YOU RECOGNIZE AN EFFECTIVE TEACHER WHEN YOU RECRUIT ONE?
Jonah E. Rockoff
Brian A. Jacob
Thomas J. Kane
Douglas O. Staiger
Working Paper 14485
NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
The authors would like first to thank Jon Fullerton, who helped us greatly in the design and implementation
of the survey used in this analysis. We also thank a number of individuals who made the survey possible,
including Betsy Arons, Vicki Bernstein, Nate Brown, Doug Jaffe, Leigh McGuigan, Amy McIntosh,
Joe Meglino, and Ranjeet Singh of the New York City Department of Education, Delia Stafford and
Martin Haberman of the Haberman Foundation, and Heather Hill of the Harvard Graduate School
of Education. Ellen Viruleg, Stephanie Rennane, and Robert Lindsley provided outstanding research
assistance. We are grateful to the Spencer Foundation and the Carnegie Corporation for generous
financial support. The views expressed herein are those of the author(s) and do not necessarily reflect
the views of the National Bureau of Economic Research.
© 2008 by Jonah E. Rockoff, Brian A. Jacob, Thomas J. Kane, and Douglas O. Staiger. All rights
reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission
provided that full credit, including © notice, is given to the source.
Can You Recognize an Effective Teacher When You Recruit One?
Jonah E. Rockoff, Brian A. Jacob, Thomas J. Kane, and Douglas O. Staiger
NBER Working Paper No. 14485
JEL No. I21,J45
Research on the relationship between teachers’ characteristics and teacher effectiveness has been underway
for over a century, yet little progress has been made in linking teacher quality with factors observable
at the time of hire. However, most research has examined a relatively small set of characteristics that
are collected by school administrators in order to satisfy legal requirements and set salaries. To extend
this literature, we administered an in-depth survey to new math teachers in New York City and collected
information on a number of non-traditional predictors of effectiveness including teaching specific
content knowledge, cognitive ability, personality traits, feelings of self-efficacy, and scores on a commercially
available teacher selection instrument. Individually, we find that only a few of these predictors have
statistically significant relationships with student and teacher outcomes. However, when all of these
variables are combined into two primary factors summarizing cognitive and non-cognitive teacher
skills, we find that both factors have a modest and statistically significant relationship with student
and teacher outcomes, particularly with student test scores. These results suggest that, while there
may be no single factor that can predict success in teaching, using a broad set of measures can help
schools improve the quality of their teachers.
Jonah E. Rockoff
Graduate School of Business
3022 Broadway #603
New York, NY 10027-6903
Brian A. Jacob
Gerald R. Ford School of Public Policy
University of Michigan
735 South State Street
Ann Arbor, MI 48109
Thomas J. Kane
Harvard Graduate School of Education
Gutman Library, Room 455
Cambridge, MA 02138
Douglas O. Staiger
Department of Economics
HB6106, 301 Rockefeller Hall
Hanover, NH 03755-3514
“And this is our present purpose: to discover, so far as possible, what elements enter into the
making of a capable teacher.”
– J.L. Meriam, Teachers College Contributions to Education No. 1 (1906)
Research on the relationship between teachers’ characteristics and teacher effectiveness
has been underway for over a century, yet little progress has been made in linking teacher quality
with factors observable at the time of hire (see reviews by Hanushek (1986, 1997) and
Greenwald et al. (1996)). Teaching experience is perhaps the only characteristic that has
consistently been found related to teacher effectiveness, but a recruitment policy of hiring only
veterans would be infeasible in most school districts. At the same time, the importance of
recruiting high quality teachers has been bolstered by recent work demonstrating substantial and
persistent variation in achievement growth among students assigned to different teachers (e.g.,
Rockoff (2004), Rivkin et al. (2005), Kane et al. (2006)), and Aaronson et al. (2007)). These
findings have led to proposals that districts pay more attention to performance in the early part of
teachers’ careers as opposed to spending more resources on recruitment and hiring (Gordon et al.
However, most research on teacher effectiveness has examined a relatively small set of
teacher characteristics, such as graduate education and certification, which are collected by
school administrators in order to satisfy legal requirements and set salaries. Like the well-known
story of a man looking for his keys under a street light—not because he dropped them nearby,
but because that is where he can see—researchers’ lack of success in predicting new teacher
performance may be driven by a narrow focus on commonly available data.
In the present study, we explore whether certain characteristics not typically collected by
school districts can predict teacher effectiveness. To do so, we administered an in-depth survey
of new elementary and middle school math teachers in New York City in the school year 2006-
2007. The survey assesses a host of teacher qualities at the time of hire, including general
cognitive ability, content knowledge, personality traits (e.g., extraversion), and personal beliefs
regarding self-efficacy. We match this survey data to administrative data on students and
teachers in New York City, which allows us to explore how both traditional (e.g., certification
type, teacher certification exam scores, selectivity of undergraduate institution) and nontraditional
measures of teacher effectiveness predict five outcomes: the achievement of teachers’
students on standardized math tests, subjective teacher performance ratings, teacher absences,
and teacher retention at both the district and school level. In addition to comparing the
predictive power of our non-traditional measures with the several traditional measures, we also
explore how well sets of variables can jointly predict teacher effectiveness.
We then investigate a commercial instrument widely used to screen candidates—the
Haberman Star Teacher Evaluation PreScreener. The Haberman PreScreener is used by a
number of large urban school districts throughout the U.S., and is intended to provide school
officials with guidance on how effective a particular candidate is likely to be in an urban
classroom. We examine what teacher characteristics are associated with high scores on the
Haberman PreScreener, and then test whether performance on this instrument predicts a variety
of teacher and student outcomes.
We find statistically significant but modest relationships between student achievement
and several non-traditional predictors of teacher effectiveness, including performance on the
Haberman selection instrument. We find marginally significant increases of about 0.02 standard
deviations in math achievement associated with one-standard deviation increases in cognitive
ability and self-efficacy. For respondents’ scores on a test of math knowledge for teaching, we
estimate an effect size of about 0.03 standard deviations and statistical significance at the 2
percent level. Scores on the Haberman PreScreener are also positively related to student
achievement, with an effect size of 0.02 standard deviations, which is marginally significant at
the 11 percent level. As a point of comparison, prior research using similar data from the DOE
(Kane et al. (2006)) found that assignment to a teacher with a year of teaching experience in the
DOE, as opposed to a true “rookie” is associated with roughly 0.04 standard deviations higher
student achievement. Interestingly, we do not find respondents’ levels of conscientiousness or
extraversion (as measured on a standard personality inventory) are significantly related to student
achievement, but they are strong predictors of subjective evaluations made of respondents. This
finding is of interest given a large literature on the impacts of worker personality on job
performance, which often uses subjective evaluations by supervisors as the performance metric.
No single metric we examine has the ability to reliably identify very large differences in
teacher effectiveness among our survey respondents. However, through the use of factor
analysis, we document how these metrics can be combined into simpler measures of cognitive
and non-cognitive skills, both of which have statistically significant relationships with student
achievement. Together, these factors have modest but economically meaningful power for
screening effective teachers at the time of hire. Our estimates suggest that students assigned to a
teacher who is one standard deviation higher on either the cognitive or non-cognitive factor have
achievement that is .033 standard deviations higher. These results suggest that schools and
school districts wishing to increase the effectiveness of their teacher workforce may be aided by
the systematic use of a broad set of information on new candidates, and particularly if they
gather information outside the realm of traditional teaching credentials. Nevertheless, our results
are also consistent with the notion that data on job performance may be a more powerful tool for
improving teacher selection than data available at the recruitment stage.
The paper proceeds as follows. In Section 2 we describe the contents of our survey of
new teachers and in Section 3 we provide details on our sample and the additional data we use to
examine student and teacher outcomes. Section 4 provides descriptive statistics on survey
respondents and their responses. We present our methodology and the results of our analysis of
traditional and non-traditional predictors in Section 5. Section 6 presents results of a factor
analysis on teacher characteristics and tests of the predictive power of these factors for student
and teacher outcomes. Section 7 concludes.
2. Survey Elements
The main focus of our analysis is an online survey of teachers who began their careers in
New York City public schools in the school year 2006-2007. The goal of this survey was to
capture a set of information that has not been widely studied in the literature on teacher
effectiveness, but has been linked to teacher productivity or productivity in other occupations by
prior research. In this section, we provide details on each of the major survey components,
describing the theory and research that motivated its inclusion in the survey. We provide
examples of many of the items in the appendix, and the full survey is available upon request.
Note that we do not review the extensive literature on more traditional predictors of teacher
effectiveness, which focuses on characteristics such as experience or certification type. For
reviews of this literature, see Jacob (2007).
2.1 A Teacher’s Cognitive Ability and Academic Success
Some researchers have found that teachers with stronger academic backgrounds produce
larger performance gains for their children (see, for example, Clotfelter et al. (2006, 2007), in
addition to the reviews cited above). However, there are also a number of studies which do not
find this relationship (e.g., Harris and Sass (2006) on graduate course work and Kane et al.
(2006) on college selectivity). In our survey, we collect a number of measures of academic
success, covering many of the measures used by prior researchers (e.g., undergraduate major,
graduate education, selectivity of undergraduate institution, etc.).1
A small number of studies have found a link between teachers’ scores on certification
examinations and teacher effectiveness (e.g., Clotfelter et al. (2006, 2007) and Goldhaber (2007),
although Harris and Sass (2006) do not find this link). Although teachers in New York State
must take several exams in order to become legally certified to teach, the New York City DOE
does not have access to teacher certification exam scores, and these scores are unlikely to be
known by district personnel making hiring decisions. According to New York State, these
exams “are for the purpose of New York State educator certification only. They are not intended
to be used for employment decisions, college admissions screening, or any other purpose.
Candidates are not obligated to provide potential employers with copies of [their] score reports.”2
The main certification test in New York State is the Liberal Arts and Science Test
(LAST), which is required for certification in all subjects and can be taken an unlimited number
of times until a teacher passes. Boyd et al. (2006, 2008a, 2008b) use whether a teacher that
passed the LAST on the first attempt as a marker of effectiveness, and find mixed results. We
We asked respondents for their undergraduate institution, and we merge this information to the Barron’s
Selectivity Index (a 1-9 scale, one being the best) from 1982. We thank Caroline Hoxby for sharing this data with
us. For a few colleges where the Barron’s rating was missing, we use Barron’s ratings from 1984.
See www.nystce.nesinc.com and ohe32.nysed.gov/tcert/ for general information on certification exams and
www.nystce.nesinc.com/pdfs/NYSTCE_ISR_back.pdf for information on the use of exam scores.
therefore asked teachers whether they passed the LAST on their first attempt and examine this
While several early studies failed to find a significant relationship between college
admissions scores and principals’ evaluations of new teachers (e.g., Maguire (1966), Ducharme
(1970)), a commonly cited study by Ferguson and Ladd (1996) did find a link between scores on
the ACT exam and student achievement growth. We therefore asked teachers about their college
entrance examination scores. While we asked specifically about both the SAT and the ACT, few
teachers reported an ACT score and, of those that did, 90 percent also reported an SAT score.
We therefore do not use the ACT in our analysis. Although nearly 80 percent of the respondents
claimed to have taken the SAT, less than one in three reported their exact scores. Anticipating
that some teachers might not remember their scores, we also allowed teachers to give their scores
in 100 point ranges, which most did, and we assign these teachers the midpoint of the reported
range (e.g., we assign a score of 550 for someone reporting a score between 500 and 600). Still,
about 50 teachers (12 percent of respondents) reported that they took the SAT but could not
remember their scores at all.
One problem with interpreting the relation between successful teaching and college
entrance exam scores is that performance on standardized achievement tests is determined by a
host of different factors: access to educational resources in childhood, parental investment in
education, personal motivation and willingness to study hard, raw intelligence, etc. In order to
separate out at least one of these proximate causes, the survey includes a direct test of cognitive
In addition to the LAST exam, teachers may also be required to pass the Assessment of Teaching Skills (ATS-W)
and a Content Specialty Test (CST) may also be required depending on subject area and certification type. For
example, the ATS-W is not required of alternatively certified teachers (e.g., TFA and Teaching Fellows). We do not
present results on the predictive power of these exam scores, but these results are available upon request. In
preliminary analyses, we found that exam scores had no significant power to predict student achievement and the
point estimates are very small and, in some cases, of the wrong sign.
ability, Raven’s Progressive Matrices Standard Version, an intelligence test that requires no
linguistic or mathematics skills.4
An illustrative item for this instrument (taken from Raven
(2000)) is shown in Appendix Figure 1. We convert scores on this cognitive ability test to
national percentiles using the distribution for a representative sample of U.S. adults ages 20-47
who completed the self-administered test at leisure (Raven et al., 2000).
2.2 Content Knowledge
A number of studies examine the relationship between content knowledge and
effectiveness, particularly in teaching mathematics (e.g., Goldhaber and Brewer (1997),
Aaronson et al. (2007)). Although the evidence on this issue is mixed, these studies use proxies
for content knowledge such as the number of courses taken in a subject, or college major. Some
math educators and researchers argue that it is not simply mathematical knowledge per se, but
the ability to express mathematical concepts in the context of classroom teaching which is
critical. Mathematical knowledge for teaching involves the ability to explain difficult
mathematical concepts in multiple ways, and to describe the intuition behind mathematical
reasoning instead of focusing exclusively on algorithms and procedures (Schulman (1986, 1987),
Wilson et al. (1987)). Motivated by this work, we measure content knowledge using an
instrument developed by researchers at the University of Michigan designed to assess this
specific type of mathematical knowledge among teachers (Hill (2006)). There is evidence of a
positive relationship between content knowledge (as measured by this instrument) and student
achievement gains in first and third grade (Hill et al. (2005)). Importantly, they also found this
The test relies on the participant’s ability to recognize and decode patterns of symbols presented in a matrix. Each
set of items becomes progressively more difficult, requiring greater cognitive capacity to encode and analyze.
Though it has been found to have a high correlation with other major tests of intelligence (Raven and Summers
(1986)), it is considered to be one of the best measures of general cognitive ability due to its non-verbal nature. The
split-half reliabilities for this test are also high, with a coefficient of .86 (Raven et al. (1983)).
measure to be a stronger predictor of student learning than other measures of teachers’
mathematical preparation. An item from this instrument is presented in Appendix Figure 2.
2.3 Personality Traits
There is a long history of studying teacher personality characteristics in the education
literature (see a review by Getzels and Jackson (1963)). While much of this work focuses on
comparing attitudes across teachers and other occupations, or across specialties within teachers, a
few studies (e.g., Washburne and Heil (1960)) linked child-friendly attitudes with positive
teaching outcomes (although no studies assess student achievement directly). While many
studies have been conducted, few definitive conclusions have been made. One reason has been
the widespread but controversial use of the Minnesota Multiphasic Personality Inventory
(MMPI) to measure teacher personality traits, even though the MMPI was designed to measure
social and behavioral problems in psychiatric patients. Getzels and Jackson (1963) find no
consistent relationship between personality traits as measured by the MMPI and measures of
teacher success. Another reason why clear predictions have been difficult in this field is the
wide variety of theories and measures of personality that abound in psychology. However,
recent decades have seen a move from theorist-driven accounts of personality (dominated by
Freud and Jung) to simple empirical measures of important dimensions of personality.
One such empirical model, the five-factor model (or “Big Five”), has emerged as a
dominant new framework for measuring personality. The Big Five personality traits are:
agreeableness, conscientiousness, emotional stability, extraversion, and openness to experience.
We are not aware of any work linking elements of the Big Five to teacher effectiveness in raising
student achievement. However, the Big Five have been used to predict job performance across a
wide variety of other occupations. Using meta-analysis, Barrick and Mount (1991) find that
conscientiousness has been linked positively to job performance across all occupational
categories. They also document a link between extraversion and job performance in occupations
requiring social interaction. Similar results are echoed in a review by Goodstein and Lanyon
(1999). Thus, we hypothesize that conscientiousness and extraversion may be significant
predictors of job performance for teachers.
Instruments used to measure the Big Five vary in length and complexity. We employ the
Big Five Inventory (BFI), developed by John et al. (1991), which consists of 44 items: 10 for
openness to new experience, 9 each for agreeableness and conscientiousness, and 8 for emotional
stability and extraversion. Each item asks respondents for their level of agreement (on a scale of
1 to 5) with a statement about themselves, and about half the items are reverse-scored. For
example, agreement with the statements “I am someone who is talkative” and “I am someone
who is reserved” are both used to measure extraversion, but the latter is reverse-scored. Each
respondent receives a score from 1 to 5 on each of the five dimensions of personality.
2.4 Teacher Beliefs and Values
The idea of self-efficacy—the belief that one can successfully produce an outcome—as
an important factor in determining whether individuals can overcome challenges and meet goals
is well established in the field of psychology (see Bandura (1977)). Moreover, a number of
researchers have examined variation in teacher self-efficacy and its correlation with student and
school outcomes (e.g., Gibson and Dembo (1984), Dembo and Gibson (1985), Woolfolk and
Hoy (1990), Raudenbush et al. (1992), Hoy and Woolfolk (1993)). This body of work generally
finds a positive relationship between self-efficacy and outcomes such as supervisor ratings, even
after controlling for some potentially confounding covariates. However, there is little work
examining the relationship between self-efficacy and student learning. One exception is an oft-
overlooked result in a well-cited study on teacher quality by Armor et al. (1976). In addition to
being one of the first studies of teacher value-added and its correlation with principal
evaluations, this paper also finds a significant positive relationship between teachers’ sense of
self-efficacy and student achievement growth.5
Following the prior work on teachers’ self-efficacy, we measure self-efficacy in two
ways: personal efficacy (i.e., belief in one’s own ability to impact student learning) and general
efficacy (i.e., belief in the ability of teachers in general to impact student learning). We use a
ten-item instrument developed by Hoy and Woolfolk (1993), adapted from earlier work by
Gibson and Dembo (1984). A simple factor analysis of teachers’ responses finds two factors,
with the general and personal efficacy items grouped as expected.
2.5 Teacher Selection Instruments
One ultimate policy goal of research on predictors of teacher effectiveness is to develop
tools which district and school administrators could use to identify the “most promising” teacher
candidates. However, there are already two commercially available and widely used instruments
whose purpose is to measure beliefs and values indicative of future success in the classroom: the
Haberman Star Teacher Evaluation PreScreener (“Haberman PreScreener”) and the Gallup
TeacherInsight Assessment (Gallup TIA). The two instruments are similar in that they both use a
short survey consistent mostly of multiple choice items to evaluate a number of teachers’
The two questions used by Armor et al. (1976) to measure efficacy are included in our measures—one as part of
the general efficacy index and one as part of the personal efficacy index. Notably, their study, like ours, uses data
on teachers’ self-efficacy collected after the start of the teachers’ careers.
Both the Haberman PreScreener and the Gallup TIA were developed by first
interviewing teachers thought to be highly effective and designing questions to capture their
attitudes and beliefs. These instruments have been used by many large urban school districts
throughout the U.S., including Atlanta, Buffalo, Cleveland, Dallas, Denver, Long Beach, Los
Angeles, Minneapolis, Nashville, Philadelphia, Pomona, San Francisco, San Diego, Tampa, and
While use of commercial selection instruments has grown considerably, there is little
systematic evidence on the power of these instruments for predicting teacher effectiveness.
Haberman (1993, 1995) has published some reports of his research, but no empirical data are
available for independent analysis. New York City recently began requiring all applicants for
teaching positions to take the TIA. In ongoing work, we are assessing how well this instrument
predicts student and teacher outcomes in the district. In this paper, we analyze the Haberman
PreScreener, which was included as a part of our survey and was scored for us by the Haberman
Foundation. Each teacher is given a categorical score of “Low,” “Average,” or “High” in each
of ten attributes (see footnote 9) and an overall score for the total number of questions answered
correctly. In their work with districts, the Haberman Foundation places teacher candidates into
four ranked categories: 1) a top group which includes candidates who answered at least 33
questions correctly, and did not receive a “low” score in any of the ten categories; 2) a second
group which includes candidates who did not receive any “low” scores but answered less than 33
questions correctly; 3) a third group which includes candidates who answered at least 33
The Haberman PreScreener is a short survey that uses 50 multiple-choice items to assess ten different attributes:
persistence, organization and planning, beliefs about the value of students learning, approach to students, approach
to at-risk students, ability to connect theory to practice, ability to survive in a bureaucracy, fallibility, explanation of
students’ success, and explanation of teacher success. Similarly, the TIA instrument uses multiple choice, Likert
scale (i.e., level of agreement from 1 to 5), and open-ended items to assess a number of teacher attributes. We have
been unable to find a list of attributes for the Gallup TIA, but an earlier Gallup instrument, the Teacher Perceiver
Interview, measured 12 attributes (Metzger and Wu, forthcoming): Mission, Empathy, Rapport drive, Individualized
perception, Listening, Investment, Input drive, Activation, Innovation, Gestalt, Objectivity, and Focus.
questions correctly, but had a “low” score in one of the ten categories; and 4) a bottom group that
consists of teachers who either (i) received one low score and answered less than 33 questions
correctly or (ii) received two or more low scores regardless of the number of questions answered
correctly. According to Haberman officials, no applicant with two or more “low” scores should
be hired, regardless of the total number of questions correct.7
Twenty-one percent of our survey respondents completing the Haberman PreScreener fell
into the top group according to the categorization system described above, while 60 percent fell
into the bottom group. In our analysis, we test whether being in the top group of teachers is
predictive of positive outcomes. However, we make use of the other variation in the data by
testing the predictive power of the total number of questions answered correctly.8
2.6 Other Teacher Characteristics
In addition to the items described above, we also asked about several other
characteristics. These included occupations prior to teaching in the DOE, weeks and hours per
week of paid and volunteer experience in various fields related to working with children (i.e.,
full-time teaching, substitute teaching, work as an education paraprofessional, tutoring, work in
after-school programs, coaching, baby-sitting, work in child care/day care, camp counselor, work
in community programs, mentor, and work in religious education), childhood setting (i.e., rural,
suburban, urban, or foreign), K-12 education (public or private), and attendance of New York
City public schools. In preliminary analyses not reported here (but available upon request), we
found no systematic and/or significant relationship between these measures and our outcomes.
Description of the Haberman scoring method is based on personal communication with Martin Haberman and
Delia Stafford in the Fall of 2007 and subsequent conversations in the Spring of 2008.
Note that this is not based on any recommendation of Martin Haberman or the Haberman Foundation.
3. Data Collection and Analysis Sample
Here we describe more carefully the administration of the survey, the administrative data
used to measure student and teacher outcomes, and the construction of our analysis sample.
3.1 Survey Administration
Due to budget constraints, we target our survey to new elementary and middle school
math teachers, a group for whom we could calculate value-added measures of effectiveness using
models that relied on prior test scores as a control. With the assistance of DOE officials, we
identified 602 new teachers with no prior experience who were identified as teaching
mathematics to students in grades four through eight (testing begins in third grade in New York
City). Some of these teachers were teaching all subjects to a single elementary class, while
others taught math to one or more classrooms of students in middle school grades.9
Ideally, we would have administered the survey to these teachers prior to the start of the
school year. However, data linking students and teachers in New York do not become available
until well past the start of the school year. In addition, some of the survey elements required us
to navigate legal copyright issues, and this caused some delay. In the end, survey invitations
went out on April 3, 2007, and teachers were given until the end of June to complete the
survey.10 The timing of the survey has implications for the interpretation of our results, and we
discuss this further below.
The survey was fairly extensive, with seven parts and over 200 items. Pilot testing of the
survey with students at the Harvard Graduate School of Education suggested that completion
In general, elementary schools in New York City include grades K-5, middle schools include grades 6-8 and high
schools include grades 9-12. However, there are schools with a variety of different grade configurations, such as K-
8, 5-8, 6-7, 6-12, etc.
10 In order to protect the confidentiality of the data, communication with teachers was done via the Human
Resources Department at the DOE. Survey invitations contained a unique link, based on a scrambled teacher
identification number, so that survey responses could be merged with other sources of data.
would require about 90 minutes. In order to compensate teachers for this substantial amount of
time, we offered a $75 payment for successful completion of the survey. Several reminders were
sent to non-respondents and non-completers between the start and end of the survey period. Of
the 602 teachers invited to complete the survey, 418 (69.4 percent) began the survey and 333
(55.3 percent) completed it entirely.11 In Section 4, we compare respondents and nonrespondents
on a variety of observable characteristics.
3.2 Administrative Data
In addition to the responses to our survey, we use data from a number of other sources in
our analysis. Administrative data from the DOE payroll system provides us with information on
all full-time teachers in the DOE in September, November, and May of each school year since
1999-2000. This provides information on each teacher’s gender and ethnicity, certification
route/program (i.e., whether a teacher was traditionally certified or entered via an alternative
certification program such as Teach for America or the New York City Teaching Fellows),
teaching experience (as proxied by their position on a salary schedule), number of absences, and
whether they have left the DOE or switched schools.
We measure student achievement using data on standardized test scores in math for
students in grades four through eight. These data follow students over time and provide links to
their math teachers. The student data we possess also include information on demographics,
receipt of free and reduced price lunch, and status for special education and English Language
Learner services. A full description of the data can be found in Kane et al. (2006).
A small but growing literature demonstrates a significant relationship between objective
measures of teacher performance and subjective evaluations of teacher quality made during a
11 Respondents include all teachers who began the survey, including 15 teachers who began the survey but did not
complete any of the main sections. Placing these 15 teachers in the non-respondent category does not noticeably our
comparisons of respondents and non-respondents (Table 1).
teacher’s career (e.g., Murnane (1975), Armor et al. (1979), Harris and Sass (2008), and Jacob
and Lefgren (2008)). One of the outcomes we examine is a subjective evaluation of teacher
effectiveness by a mentor who meets with the teacher weekly and makes classroom observations.
These data come from a centrally administered program to assist new teachers, which was
created to comply with a New York State law requiring mentoring (see Rockoff (2008)). We do
not have evaluations for new teachers in a number of schools that were exempt from the
centralized mentoring program due to their status as an “Empowerment School,” which gave
more programmatic choice to principals.12
Mentors are each assigned a group of roughly 15-20 teachers, usually spread across a
number of different schools. In addition to working with teachers, mentors submit monthly
summative evaluations of teachers’ skills on a five point scale ranging from “beginning” to
“innovating.” In practice, almost all teachers are rated “beginning” at the start of the school
year, and some teachers are missing ratings for a subset of months. In order to have meaningful
variation in evaluations, we concentrate on evaluations submitted towards the end of the year.
To avoid bias due to either the timing of evaluations or the leniency of mentors, we subtract the
average rating given by each mentor in each month from an individual teacher’s rating (i.e., we
normalize ratings by mentor-month cell). We then average over ratings given in the months of
April, May, and June. For the teachers who were not rated in those months (less than two
percent of teachers with any recorded evaluations), we use ratings averaged over January,
February, and March.
In order to control for observable school characteristics in some of our analyses, we
collected school-level information from the National Center for Education Statistics’ Common
Core of Data. This includes school level data on student ethnicity, gender, and eligibility for free
12 For more information on Empowerment schools, see http://schools.nyc.gov/Offices/Empowerment/ .
lunch of students, as well as the school’s eligibility for Title I resources, pupil-teacher ratio, and
grade composition. In order to better control for differences across schools that are unobservable
in the CCD data but related to local neighborhood characteristics, we identified the zip code of
each school in our sample, which allows us to include school zip code fixed effects.
3.3 Our Analysis Sample
While our analysis focuses on the 418 teachers who responded to our survey, we include
other teachers in our analysis in order to help identify coefficients on variables other than those
from our survey (e.g., student and school characteristics). Specifically, when examining teacher
outcomes (subjective evaluations, absences, and retention) we include data on the 184 teachers
who were asked to take the survey but did not respond and a set of 4,275 other new teachers.
This set of other new teachers are defined as those with no prior teaching experience that started
in the school year 2006-2007 who were present in the DOE payroll files in both November and
May, did not teach in a special program (e.g., extended high school for adults), were linked with
school level data on student characteristics, and were not asked to take our survey.13 For each of
the outcomes that we explore, our sample naturally includes only those teachers with valid
outcome data. We have attrition data for all 4,877 teachers in our sample, but lack absence data
for 19 teachers. For mentor ratings, we have data on 3,030 teachers (62 percent of our sample).
The fraction of teachers with mentor evaluations is somewhat higher among teachers who
responded to our survey (75 percent) or were asked to take our survey but did not respond (73
percent) than among those who were not asked (60 percent). Nearly 70 percent of the missing
evaluations are due to teachers working in Empowerment schools, which did not participate in
13 Conditioning on presence in November and May ensures that, like the teachers invited to the survey, the other
new teachers were hired close to the start of the school year and did not leave before the end of the year. While
conditioning on presence in payroll in September and May might seem more appropriate, the timing of record
updating in the DOE is such that many new hires are not present in the September payroll data.
the centralized mentoring program. Of the remaining teachers, 83 percent are merged with data
from the mentoring program, which is in line with earlier program years (see Rockoff (2008))
and is likely due to administrative errors and late hiring.14
For our analysis of student achievement, we use a slightly different sample. Specifically,
we include all students and teachers in the value-added grades (grades 4-8) during the school
year 2006-2007. We include these additional classrooms in order to gain better estimates of the
coefficients on important control variables, such as prior student achievement, participation in
English Language Learner and special education programs, etc. In addition, we restrict our
analysis using the same rules as in Kane et al. (2006): excluding schools where we could not
successfully merge at least 75 percent of the classes with teachers and schools serving only
special education students (176 out of 1169 schools), classrooms that could not be linked to a
teacher (less than 2 percent of classrooms in the remaining sample), where more than 25 percent
of students received special education services (19 percent of classrooms in the remaining
sample, 73 percent of which had only special education students), which had at least 7 and no
more than 45 students (eliminating 10 percent of the remaining classrooms), and whose assigned
teacher left mid-year or switched schools (2 percent of remaining classrooms). This leaves us
with just over 13,000 classrooms in 988 schools. In total, we are unable to examine math valueadded
for 43 of our 418 survey respondents: 7 were not linked to students in our testing data, 2
taught in schools for which we could not match at least 75 percent of students to teachers, 5
switched schools during the year, and 36 taught in classrooms where more than 25 percent of the
students were classified as receiving special education services.
14 The fraction of teachers with mentor evaluations among teachers not in empowerment schools is also higher
among teachers who responded to our survey (91 percent) or were asked to take our survey but did not respond (92
percent) than among those who were not asked (82 percent).
4. Descriptive Statistics
Table 1 provides summary statistics broken down into three groups: survey respondents,
new teachers who were invited and did not respond, and other new teachers hired in 2006-2007
that were not invited to participate in the survey. The third column provides P-values on tests of
whether there is a statistically significant difference in the mean of a characteristic between
respondents and non-respondents. Of the 18 teacher and school characteristics listed in the table,
there are two on which the respondents and non-respondents are significantly different at the five
percent level or lower. Relative to non-respondents, respondents were more likely to be female
(78 percent vs. 66 percent), and were less likely to come from the Teach for America program
(15 percent vs. 22 percent). Though the p-value is slightly above 0.05, it is also noteworthy that
survey respondents were given higher subjective evaluations by their mentors (0.04 vs. -0.05).
While we do not report statistical tests of differences between teachers not invited to take our
survey and those that were, they are fairly similar along characteristics to the teachers who were
invited to take the survey.15
Summary statistics on outcomes for all three groups are shown at the top of Table 1.
Absences for new teachers averaged 5.7 over the school year for teachers asked to take our
survey and 6.4 for those who were not asked. The standard deviation of absences among all
teachers in our sample is 4.7, but the distribution is skewed, ranging from 0 to 41. Among
survey respondents, 8.1 percent did not return to teaching in the DOE the following school year,
similar to 6.5 percent for non-respondents and 7.4 percent for other new teachers. An additional
15 Though not shown in Table 1, far more teachers invited to take the survey were licensed in math, but this is not
surprising given that we targeted our survey to math teachers. We have also compared the characteristics of teachers
who completed to the survey to those that began but did not complete (results available upon request). Relative to
individuals who completed the entire survey, individuals that started but did not complete the survey were more
likely to be non-White and less likely to come from the Teach for America program.
8.9 percent of respondents returned to teach in a different school within the DOE, compared with
8.2 percent of non-respondents and 8.1 percent of other new teachers.
Table 2 presents summary statistics on variables from our survey, grouped by broad
themes. The number of non-missing observations varies across survey items due to varying
completion rates by respondents and the position of the item in the survey. The academic
backgrounds of survey respondents are quite varied. Approximately one in five survey
respondents majored in either math or science, and about one in six majored in education.16
However, there is considerable variation in college major between teachers assigned to students
in grades four and five (28 percent majoring in education and 3 percent in math and science) and
those assigned to grades six to eight (9 percent majoring in education and 34 percent in math and
science). Thirty-two percent of survey respondents reported having a graduate degree. Average
reported SAT scores were roughly 600 in both math and verbal with a standard deviation of
about 100 points. The fairly high averages may reflect the percentage of Teaching Fellows and
TFA corps members in our sample, and perhaps non-random selection in teachers’ willingness to
report their scores. The average Barron’s rank of respondents’ undergraduate institutions was
5.6 (on a 1-9 scale with 1 being the highest). Twelve percent of respondents’ institutions ranked
in the top three categories, with 40 percent in the middle (ranked four to six) and the remainder
from institutions ranked seven or below. Nearly all of the respondents (92.2 percent) claimed to
have passed the LAST exam on their first attempt. This is somewhat higher than the pass rates
for new teachers in the school year 2004-2005, which were less than 90 percent (Boyd et al.
16 We group all other college majors together in our analysis. About 30 percent of survey respondents majored in
political or social sciences, 13 percent in English or humanities, 9 percent in Foreign languages or communications,
7 percent in business, 5 percent in the Arts, and two percent in “Other” (i.e., they did not find a match among the 50
majors we presented as choices).
2006), but may simply reflect a continued trend of increasing pass rates for new teachers in New
The average score on the test of cognitive ability fell at the 53rd percentile relative to
national norms. The standard deviation was 26 percentile points, indicating a substantial amount
of heterogeneity in cognitive ability in our sample. Indeed, the scores for survey respondents
matched the national norms to within one point at the 25th, 50th, 75th, 90th, and 95th
percentiles. They outperformed the national distribution at the 5th and 10th percentiles, but,
given that all of these teachers must have a college degree, this is not terribly surprising.
The portion of answers answered correctly on the test of math knowledge for teaching
was 0.57 on average, with a standard deviation of 0.20. The 10th and 90th percentiles of
respondents correctly answered 33 and 83 percent, respectively. In addition to the portion
answered correctly, we estimated scaled scores for this test using item response theory. The
results of our analysis are quite similar using the scaled scores or the portion correct, and thus,
for greater transparency, we report results for the portion correct. Scores on the math knowledge
for teaching exam were positively correlated with self-reported math SAT (r=0.46), verbal SAT
(r=0.38), cognitive ability (r=0.49) and the (inverse of) Barron’s selectivity rating of
undergraduate institution (r=0.34). Interestingly, while math or science majors scored
significantly higher than education majors (60 percent vs. 49 percent correct), respondents with
majors other than education, math and science performed similarly well (60 percent correct).17
17 As an additional check on the academic background survey results, we compared scores on cognitive ability, math
content, and (self-reported) college entrance examinations for groups of teachers from different certification
pathways. On all tests, scores for teachers from the New York City Teaching Fellows program were higher than
regularly certified teachers, and scores for teachers from the Teach for America program were higher than both other
groups. This matched our expectations; both TFA and the Teaching Fellows recruit candidates from highly selective
colleges and universities, but the TFA program is generally recognized as more selective.
In Table 2 we report the raw scores (on a scale of 1-5) for all five dimensions of
personality from the Big Five Inventory, though in our analysis below we restrict our attention to
conscientiousness and extraversion. While these summary statistics are difficult to interpret, to
our knowledge, there is no standard benchmark for the Big Five. The National Survey of Midlife
Development in the United States, 1995-1996, did collect data on the Big Five for a
representative sample of English-speaking, non-institutionalized, U.S. adults between the ages of
25 and 74.18 However, the two sets of results are not directly comparable because the exact
number and wording of the items in this survey were not identical to ours and because responses
were given on a scale of 1 to 4 (see Lachman and Weaver (1997)). Therefore, rather than ask
whether survey respondents score higher or lower than the national sample on a particular trait,
we examine whether the ratio of a particular trait to the other traits among our survey
respondents is greater or less than ratios for the national sample. Using this (admittedly
informal) method, we find that our survey respondents have relatively higher scores on
emotional stability, lower scores on extraversion, and similar scores on conscientiousness,
agreeableness, and openness to new experiences.19 However, there are no striking differences
between the two samples’ scores.
Finding a benchmark for the self-efficacy scores is also difficult, so we compare our
survey respondents’ average scores (3.8 for personal efficacy and 3.2 for general efficacy) to
samples in the prior literature. Our respondents’ scores are lower than teachers surveyed in
Woolfolk and Hoy (1990) and Hoy and Woolfolk (1993), where samples averaged, respectively,
4.2 and 4.7 for general efficacy and 3.6 and 3.8 for personal efficacy. However, the variation in
scores within all three groups is of similar magnitude. The correlation between personal and
18 This data is available from ICPSR as Study No. 2760.
19 The mean scores for the nationally representative sample on the 1-4 scale were 3.48 for agreeableness, 3.42 for
conscientiousness, 3.20 for extraversion, 2.76 for emotional stability, and 3.02 for openness to new experiences.
general efficacy our sample is 0.15, which is identical to the sample in Hoy and Woolfolk (1993)
and similar to the correlation of 0.07 found for the sample in Woolfolk and Hoy (1990).
Among teachers who completed the Haberman PreScreener, just over 20 percent fell into
the top group of candidates according to the recommended classification system. The average
total number of items answered correctly (out of 50) was about 32, with a standard deviation of
about five points. Haberman cites 32 as a median score, so that our sample of teachers (for
whom the mean and median are both 32) seems to have scored similarly to the population of
individuals in other districts that have completed the Haberman instrument.
5. Predictors of Teacher and Student Outcomes
In this section, we examine how well a series of traditional and non-traditional teacher
characteristics predict student and teacher outcomes. In Section 5.1, we outline the statistical
methodology we use, highlighting some of the limitations of our approach. In Section 5.2, we
present results that present each predictor separately in order to measure the overall relationship
of each predictor with teacher and student outcomes. In Section 5.3, we investigate the
correlates of performance on the Haberman PreScreener and the power of this instrument to
predict teacher and student outcomes.
5.1 Empirical Strategy
Our primary goal is to determine which, if any, measurable teacher characteristics predict
various teacher and student outcomes. When we consider teacher-level outcomes (e.g., number
of teacher absences in a given year, mentor’s rating of the teacher), we will estimate a regression
like the one shown by Equation 1, where Yj is the outcome for teacher j in school k, Pj is a
predictor of teacher effectiveness, Xj (SCjk) are other teacher (school) characteristics that are
included as control variables in certain specifications, and εj is an idiosyncratic error term.
(1) Yj=α+δPj +βXj + γSCjk + εj
As mentioned earlier, we include in our analysis a large number of new teachers who were not
asked to take our survey. For these teachers, and for teachers who did not respond to the survey
invitation or did not complete a particular part, we set the predictor variable to zero and include
an indicator for whether an actual survey response was missing. We do this in order to obtain
better estimates of the coefficients on our school-level controls. To the extent that factors such
as school poverty (i.e., the fraction of students eligible for free lunch) influences outcomes such
as teacher absences, the exclusion of these controls (or mis-measurement of the true effect of
these characteristics) may lead to biased estimates of our key predictors.
When examining student achievement data, we estimate a similar specification (shown in
Equation 2) where Aijk is the achievement level of student i, assigned to teacher j in school k, and
Si represents a set of controls for student characteristics, including prior achievement.
(2) Aij=α+δPj +βXj +γSCk + λSi +εijk
Following the approach described above, we include students taught by teachers who were not
invited to take the survey or did not respond in order to identify the coefficients on student and
school characteristics. As with teacher outcomes, we use indicators for teachers with missing
survey data and set predictor variables to zero for the students assigned to these teachers.
We examine five dependent variables in our analysis: student test scores in math, teacher
absences, subjective evaluations of teachers, whether a teacher returns to the DOE the following
year, and whether a teacher returns to the same school the following year. Both test scores and
subjective evaluations have been normalized to have a standard deviation of one so that
coefficients can be readily interpreted. In order to maximize our statistical power in examining
predictors from our survey, we include all individuals with non-missing data, so that, while our
sample size does not vary across the specifications, the true number of teachers with identifying
variation fluctuates slightly. For simplicity in exposition, we use linear regression analysis in all
cases, and report coefficients and standard errors clustered at the school level. We find very
similar results to those presented here using negative binomial regressions to examine absences
and conditional logistic regression to examine teacher retention.
In all regressions, we include controls for the characteristics of schools (from the
Common Core of Data), school zip code fixed effects, and grade level fixed effects. In the
student achievement specifications, we drop the school average characteristics from the CCD but
include controls for individual students’ prior student test scores (specifically, cubic polynomials
in both prior math and reading scores, interacted with grade level), student demographics
(gender, ethnicity, participation in free lunch, special education, and English Language Learner
programs, and the number of absences and suspensions in the prior school year), as well as
classroom and school averages of these student characteristics. We regard this specification as
generating valid estimates of the relationship between survey variables and teacher effectiveness.
While we recognized that the inclusion of school fixed effects would be a more robust
methodology, only 24 percent of the schools that had any survey respondents had more than one,
making within-school identification impracticable.
Before presenting our results, it is worth considering several issues with regard to how
our estimates should be interpreted. First, even with our in-depth survey, we measure a limited
set of teacher characteristics and thus our models will miss many characteristics that might
influence student learning (e.g., a teacher’s empathy, toughness, love for children, personal
charisma, connections to others with teaching experience, etc.). Hence, one might be concerned
that our analysis could suffer from a standard omitted variable bias. Suppose, for example, that
extraversion and empathy are positively correlated and both positively impact student
achievement. In this case, the exclusion of empathy from our estimates may lead us to overstate
the effect of extraversion on student performance.
While this is a potential concern, recall that a key objective of our exercise is the
identification of potentially effective measures for the purpose of hiring. In this respect, we are
concerned entirely with “predicting” effectiveness, in which case a reliable correlation may still
be useful for teacher hiring. If extraversion and empathy were strongly correlated in a pool of
applicants, for example, then one could improve student outcomes by hiring those with high
levels of extraversion even if empathy were the factor that influenced student learning. One
might be able to improve student outcomes even more if one knew the importance of empathy
and could measure it, but this does not diminish the value of knowing the bivariate correlation
between extraversion and student performance.20
A second and more serious concern stems from the fact that our analysis includes only
those teachers who were hired to teach in the DOE, and not the full set of individuals who
applied for teaching positions. To the extent that school and district officials are purposefully
selecting teachers and can select the most effective candidates, the hiring process itself may
introduce selection bias. For example, suppose that teacher conscientiousness were positively
associated with student performance. In this case, one would expect schools to hire candidates
with greater levels of conscientiousness, on average. However, if school officials hire a
candidate with a low degree of conscientiousness, it is likely that this individual is particularly
strong in some other way. Since we cannot observe and control for all other potential factors
used in hiring that might influence student outcomes, this type of selective hiring on the part of
20 In addition, if one knew the true “structural” relationship between teacher characteristics and effectiveness, then
one might develop professional development to enhance those characteristics that lead to effectiveness.
school administrators will bias our results towards zero. However, this type of bias only occurs if
the school district had access to better information than is observed in our data when they
selected teachers. Although school district officials may have had access to additional
information (e.g., from face-to-face interviews with teachers), they are unlikely to have had
access to many of the measures we analyze.
A third concern stems from the timing of our survey. As noted earlier, a variety of
logistical problems delayed the administration of our survey until April 2007. One might be
concerned that some of our estimates reflect reverse causality (i.e., a teacher’s success or lack
thereof during the school year might have influenced his or her survey responses, rather than the
survey responses predicting relative success). This is not a concern for the background variables
(e.g., type of certification, college attended), and is unlikely to be a large concern for predictors
such as the personality measures that purportedly reflect more permanent individual traits. On
the other hand, reverse causality is a particular concern with regard to the teaching efficacy
measures. To the extent that the experience of teaching (and the successes or failures that come
with it) influence how individuals respond to the Haberman instrument, one should be cautious
about interpreting the coefficients on this measure as well.
5.2 The Power of Individual Predictors of Teacher Effectiveness
Table 3 shows results for the power of traditional credentials for predicting each of our
five outcomes measures. Within each column, dotted lines separate coefficient estimates from
regressions in which we include a single predictor or group of related predictors. The first
column presents results for student achievement in math, our primary outcome of interest.
Consistent with many other researchers, we find no significant relationship between graduate
education and teacher effectiveness; indeed, the coefficient is negative. We do not find that
respondents who passed the main state certification “basic skills” exam – the Liberal Arts and
Science Test (LAST) – on the first attempt are significantly more effective, but it is worth noting
that very few survey respondents (8 percent) reported failing this exam. We also tested the
predictive power of respondents self-reported certification test scores, but in no case did these
approach statistical significance (results available upon request).
When comparing alternatively certified teachers to traditionally certified among the
survey respondents, we find that teaching fellows are less effective (-0.05 standard deviations, pvalue
= 0.09) and Teach for America corps members are more effective (0.04 standard deviations
(p-value = 0.15).21 While the result on TFA is consistent with other findings (Decker et al.
(2004), Boyd et al. (2006), Kane et al. (2006)), the negative finding for teaching fellows
contrasts with earlier work (Boyd et al. (2006), Kane et al. (2006)). Non-random selection of
survey respondents does not drive this result, as the coefficient does not change when we use
identifying variation on all teachers who were asked to take the survey, as opposed to only
survey respondents. However, the negative finding on Teaching Fellows does disappear when
we use identifying variation in the certification pathway of all teachers, i.e., including teachers
(both fellows and non-fellows) hired in earlier years. Thus, it appears to be the case that either
this particular group of Teaching Fellows is relatively less effective than earlier cohorts, or that
the gains to experience for Teaching Fellows are greater than for other teachers. Although we
cannot distinguish these two explanations without additional data, Kane et al. (2006) present
some evidence in support of the latter hypothesis.
Students’ test scores growth was greater on average with respondents who majored in
math or science (0.04 standard deviations, p-value = 0.2) and slightly lower with respondents
21 While we include controls for other alternative route programs (e.g., the Peace Corps Fellows) there are far fewer
teachers in these programs and only a handful in our survey sample, and we do not report their coefficients.
who majored in education (-0.009, p-value = 0.79); we cannot reject that the coefficients are
equal (p-value = 0.24). Respondents’ self-reported SAT math and verbal scores are also not
significantly related to teacher effectiveness. However, the selectivity of respondents’
undergraduate institutions, as measured by the Barron’s scale, is positive and marginally
significant (p-value = 0.08). The positive, albeit small, relationship between college selectivity
and teacher effectiveness has been found in other studies (e.g., Clotfelter et al. (2007), Boyd et
al. (2008a)). The lack of statistical significance for SAT scores contrasts with findings from
other research, but it is worth pointing out again that these scores are self-reported and often
reported in ranges, so that measurement error (both classical and systematic) may be pushing the
coefficient estimates towards zero.
Turning to the teacher level outcomes in Table 3, the only traditional credential that is
related to subjective evaluations is college selectivity, with 0.2 standard deviation lower
evaluations given to respondents that attended a college with a ranking one standard deviation
above average. We find no statistically significant difference in the average evaluation given to
respondents that were alternatively certified vs. traditionally certified. We do, however, find that
teaching fellows were absent approximately 1 day more on average than other respondents, and
that math and science majors were absent about 1.2 days less. No other traditional credentials
were significant predictors of absences.
With regard to retention, we find negative effects for having a graduate degree (-0.05, pvalue
= 0.13) and being an education major (-.10, p-value = 0.02) on returning to teach in the
DOE the following year, and positive effects for teaching fellows and TFA corps members (0.12
and 0.13, respectively, with p-values below 0.001). These results support the notion that
teachers with more outside job opportunities are more likely to leave teaching in New York, but
may also reflect the particular nature of teaching fellows selection (in which commitment is a
consideration) and the TFA program (for which there is an explicit two year commitment). Firstyear
retention rates for TFA corps members, before their commitment has ended, are typically
quite high, but retention after the second year is markedly lower (see Kane et al. (2006)).
Conditional on returning to teach in the DOE, TFA corps members are also more likely to return
to the same school. This may, however, be driven by the fact that TFA works directly with a
limited number of schools to fill positions in high needs areas.
Table 4 presents results on the predictive power of the non-traditional measures gathered
in our survey. All of these measures have been normalized, so that the coefficients can be
interpreted as the estimated effect of moving one standard deviation in the distribution of the
predictor. Again, within each column, dotted lines separate coefficient estimates from
regressions in which we include a single predictor or group of related predictors. As above, note
that each row reflects impacts that are not conditional on any of the other predictors shown. That
is, conditional on the school and student controls mentioned earlier, one can think of these as
bivariate correlations between a single predictor and the outcome. As hypothesized, the
coefficients on these predictors are all positive, but they vary in size and statistical significance.
Respondents’ scores on the test of cognitive ability are marginally significant (p-value = 0.17)
with a coefficient of 0.016, suggesting that cognitive ability may bear some relation to teacher
effectiveness. Math knowledge for teaching is more strongly related to math achievement, with
a coefficient of 0.028 which is statistically significant at the 2 percent level. This gives support
to the work by Hill et al. (2005), who found this instrument to be a significant predictor of
teacher effectiveness and a better predictor than other measures of teachers’ math training.
The coefficients on conscientiousness (0.011) and extraversion (0.007) are positive, but
not significant at conventional levels (p-values of 0.32 and 0.52, respectively). For general and
personal efficacy, we also find positive coefficients (0.017 and 0.012, respectively) with
marginal significance on general efficacy (p-value = 0.15). Overall, these results give mild
support to the idea that teachers’ personalities and attitudes are related to teacher effectiveness.22
Interestingly, when we consider the relationship between these non-traditional measures
and the subjective evaluations of teachers provided by mentors, we find very different results.
Subjective evaluations are significantly higher for respondents with high levels of
conscientiousness, extraversion, and high levels of personal efficacy, and the coefficients are
quite large, ranging from 0.19 to 0.22. In contrast, the evaluations bear little relation to the three
non-traditional variables that were (marginally) significant predictors of math achievement,
though these coefficients are positive.
Given the contrasting results for math achievement and evaluations, it is important to
point out that when subjective evaluations are used as a predictor of math achievement, we find
that an increase of one-standard deviation in the evaluation is associated with a 0.05 standard
deviation increase in math test scores, which is a statistically and economically significant
effect.23 So, while at least a portion of the variation in evaluations is based on observable
differences in teachers’ abilities to raise student achievement, another portion of the variance in
22 We also test whether math achievement was higher among students assigned to teachers who placed greater
emphasis on teaching skills related to test performance or who felt that the state standardized tests were good
measures of students’ knowledge and skills. As mentioned above, we collected these measures to try to address a
concern that higher test score growth among students may simply reflect whether or not a teacher focuses on the test
as an important outcome. However, the point estimates on both of these variables are negative, with the coefficient
on whether state tests are good measures of skills being statistically significant. It is not clear why students perform
worse with teachers who believe the state tests are good measures of students’ knowledge, but these estimates
provide some support for the notion that teacher effectiveness as measured by value-added on test scores is not
simply an artifact of variation in the degree to which teachers focus on the skills measured by the tests. 23 Author’s calculations are available upon request. The use of these subjective evaluations by mentors as a means
for identifying effective teachers after the recruitment stage is the subject of ongoing research by one of the authors.
evaluations is clearly due to factors unrelated to the ability to raise student test scores in math.
We regard this as an important finding given the large literature on personality as a predictor of
worker productivity. Most of the studies in this literature use subjective evaluations of employee
performance by supervisors as the outcome of interest. Our findings here suggest that subjective
evaluations may be driven by both worker productivity and other worker characteristics, but that
some worker characteristics that correlate with evaluations may be unrelated to productivity.
With regard to absences, respondents with cognitive ability scores or math knowledge for
teaching scores one standard deviation above average were absent 0.4 days less.24 Respondents
with general efficacy scores one standard deviation above average were more likely to return to
the DOE. As mentioned above, it is possible that responses to the efficacy instrument are
influenced by the respondents’ teaching experiences. At a minimum, this result then suggests
that a teacher’s willingness to stay in New York is correlated with feelings about self-efficacy.
However, it is worth noting that the questions regarding personal efficacy, as opposed to general
efficacy, are more focused on the teacher’s own ability to succeed in the classroom, yet the
retention result shows up for general efficacy, as opposed to personal.
Overall, the results presented in Tables 3 and 4 suggest that both traditional and nontraditional
predictors may be associated with teacher performance in their first year as measured
by student achievement and teacher evaluations, absences and turnover. However, there are a
number of reasons to be cautious about these results. First, while most of the associations are in
the expected direction, only a few are statistically significant. Given the large number of
coefficients being considered, any reasonable adjustment for testing multiple hypotheses would
make these associations appear even less significant. Second, even the fact that many of the
24 Because the distribution of absences is skewed, we also examined the natural log of absences and an indicator for
having 8 or more absences (corresponding to the 75th percentile or higher) and found similar qualitative results.
coefficients are in the expected direction may simply reflect the fact that many of the predictors
are capturing similar underlying characteristics (so these estimates are not independent tests).
Finally, the magnitudes of these effects, for math achievement in particular, are fairly modest
relative to the differences that are known to exist across teachers. For example, Kane et al.
(2006) estimate a standard deviation of teacher effects on math achievement to be roughly 0.10
student level standard deviations. Thus, even the largest coefficient we estimate for math
achievement (.028 on math knowledge for teaching) implies that we are predicting less than 8%
of the teacher-level variation.
5.3 The Haberman PreScreener
The analysis above is largely exploratory, with the ultimate aim of identifying a variety
of predictors that school officials might use to hire teachers who will be more effective in the
classroom. As we noted earlier, there are several commercial teacher-screening instruments
currently in use. In this section, we examine one of the most popular of such tools, the
Haberman PreScreener. We first explore what characteristics and traits the Haberman
PreScreener captures, and then determine how well it predicts student and teacher outcomes.
Unlike the other non-traditional measures in our survey, the Haberman PreScreener is
designed to evaluate a number of characteristics of teachers simultaneously. Before we examine
its relation to student and teacher outcomes, we use regression analysis to investigate how
performance on this instrument is related to the demographic variables, traditional credentials,
and non-traditional measures of teacher effectiveness included in Tables 3 and 4. Our dependent
variables are whether the respondent placed in the “top group” using Haberman’s method of
screening candidates (i.e., a total score above 32 and zero “low” scores in any of ten categories)
and the respondent’s total score. We present results that include each measure as a single
predictor in separate regressions that also control for grade level taught and the school average
characteristics from the CCD we used as control variables in Tables 3 and 4. We use a probit
regression for whether a respondent is in the top group and report marginal effects; results using
OLS are quite similar.
Performance on the Haberman PreScreener is significantly related to a number of these
variables (Table 5). Among the traditional credentials, performance on the Haberman is higher
for respondents who passed the LAST on their first attempt and for those who have higher SAT
verbal scores. Every non-traditional credential is positively related to performance on the
Haberman PreScreener, and all save Extraversion are statistically significant predictors of at least
one of the two metrics. 25 Thus, as we expected, the questions on the Haberman Pre-screener are
designed to pick up on a number of the characteristics that prior research has put forth as
predictors of teacher effectiveness.
We then use the same specification here as we used for the other predictor variables to
estimate the relationship between performance on the Haberman PreScreener and student
achievement, subjective evaluations, absences, and retention (Table 6). Again, we use two
measures of performance: being in the top group of candidates and total score. While we do not
find that being in the top group of candidates is significantly related to our outcome variables, we
do find stronger relationships when examining respondents’ total scores. A one standard
deviation increase in the score on the Haberman PreScreener is associated with a 0.023 standard
25 At first glance, it is somewhat puzzling that the results for being in the top group of candidates and the total score
do not move in lock step. However, it is important to recall that, in order to be in the top group, candidates cannot
have a low score on any of ten attributes. Because only a small subset of the 50 questions focus on each attribute, it
is quite possible to answer most questions correctly while still running afoul of this rule. In our sample, there are
three attributes for which respondents were very likely to have a low score—“Approach to Students” (59 percent
low), “At Risk Students” (56 percent low), and “Explains Teacher Success” (50 percent low). Moreover, 69 percent
of respondents scored low on at least one of these attributes and there were no low scores on any attribute for the
other 31 percent of our respondents. While the 69 percent of respondents with at least one low score had lower total
scores than the other 31 percent of respondents, the difference—about four points—was only about 0.7 standard
deviations in total score. Thus, the distributions of total scores for these two groups overlap quite a bit.
deviation increase in math achievement that is marginally significant (p-value 0.11) and a 0.14
standard deviation increase in subjective evaluation (p-value = 0.03). Increases in the score were
also associated with a greater propensity to return to teaching the following year, although they
also predicted a higher probability of transferring to another school within the DOE conditional
on returning to teach.26 While these results should be taken with caution due to the timing of our
survey, they lend some support to the notion that this instrument can identify characteristics that
are correlated with teacher quality.
6. Factor Analysis and Predictions from Underlying Traits
The results presented above characterize the predictive power of various teacher
characteristics taken individually. However, many of these elements are positively correlated
and may serve as noisy measures of a small number of underlying traits. If so, then combining
several measures may yield a more reliable estimate of the underlying traits, and thus provide
more consistent predictive power for teacher and student outcomes. Therefore, we estimate a
factor model, which models all of our measures as noisy estimates of a few underlying traits, and
use the results to construct more reliable estimates of the underlying traits (the factors). We then
use these estimated factors as predictors in a simplified analysis.
In the factor analysis, we include all of the variables whose coefficients are shown in
Tables 3 and 4, as well as the Haberman total score. We do not include the indicator for being in
the top group according to Haberman scoring methodology; the total score has a stronger
relationship with the outcome measures and we prefer the greater variation afforded by this
26 The unconditional effect on returning to teach in the same school is not significantly different from zero.
The variables we include the factor analysis are missing for some teachers. Traditional
factor analysis fits the factor model to the correlation matrix constructed using only observations
with complete data. In order to use all of the available data, we instead estimated the factor
analysis using the pair-wise item correlation matrix. We apply a Promax rotation to the factor
loadings. The resulting factors may be correlated with each other, but maximize the extent to
which each measure is associated with a single factor. To choose the number of factors, we use
an eigenvalue cut-off of one, a commonly used standard in this methodology.
The results of the factor analysis are reported in Table 7. The factor analysis results in
two factors, which we call “cognitive skills” and “non-cognitive skills.” The six variables with
the largest positive loadings on the first factor are all reasonable proxies for cognitive skills:
being a TFA corps member, attending a more selective college, SAT math score, SAT verbal
score, cognitive ability as measured by the Raven IQ test, and math knowledge for teaching. The
five variables with the largest positive loadings on the second factor are all reasonable proxies
for other non-cognitive skills important to teachers: extraversion, conscientiousness, personal
efficacy, general efficacy and the Haberman total score. Interestingly, being a teaching fellow
(and, to a lesser extent, majoring in math or science) have considerable negative loadings on the
non-cognitive factor, while majoring in education has a considerable negative loading on the
The measures that primarily load on a single factor are noisy estimates of that factor. In
this case, the square of the loading coefficient reported in Table 7 is equal to the measure’s
reliability as an estimate of the underlying factor (the percent of the total variance in the measure
due to the factor). Thus, the six measures that have loadings on the cognitive skills factor of
around 0.6 have reliability as measures of cognitive skills of around 36%. Simply averaging
across these six measures would reduce the noise by 1/6, and increase the reliability to around
80%. A similar calculation for the 6 measures with loadings above 0.35 for the non-cognitive
factor (including the negative of teaching fellow) increases the reliability from around 20% for
any individual measure to over 60% for the average of the six measures.
We use the results of the factor analysis to predict each factor using all of the information
available on each teacher. Most of these teachers only had a subset of the measures that were
included in the factor model reported in Table 7, but the structure of the factor model allowed us
to predict the underlying factors conditional on whatever measures were available. These
predictions are linear combinations of all the measures from Table 7, and are the best linear
unbiased predictor of the underlying factors. Therefore, they are in the same units as the
underlying factor (which are normalized to have standard deviation equal to one). In total, we
are able to measure these factors for a total of 403 teachers. We present results using the
predictions from the factor model as measures of teachers’ cognitive and non-cognitive skills
because this is the more standard approach in the use of factor analysis. However, these
predictions are highly correlated with the simple average of the six measures with largest
loadings on each factor.
In Table 8, we use the predicted factors as predictive variables in regressions of student
test scores and teacher level outcomes, using the same specifications as with the single predictors
but including both factors together. Unlike for some of our non-traditional predictors, we do not
standardize the factors to have a mean zero and standard deviation equal to one. Thus, the
coefficients are indicative of a 1 point change in the underlying factor. It thus reflects our best
estimate of the impact of a one standard deviation of cognitive or non-cognitive skills in the
population of new teachers, not solely among survey respondents. Both factors are positively
and significantly associated with math achievement. Increasing either cognitive or non-cognitive
skills by one point is associated with increases in student achievement of 0.033 standard
deviations. Interestingly, only non-cognitive skills have a significant positive relationship with
subjective evaluations, while cognitive skills have a significant positive association with
retention within the DOE.
The effects of cognitive and non-cognitive skills on student achievement are modest but
still economically important. Moreover, our ability to measure these two sets of skills is greatly
improved by the use of the non-traditional measures gathered in our survey. To illustrate both of
these points, we take the estimates from Column 1 of Table 8 and assign each teacher respondent
the predicted impact on student achievement associated with these two factors. We also estimate
the cognitive and non-cognitive factors using only the traditional credentials (i.e., we act as if the
non-traditional measures were unavailable for our survey respondents), repeat our regression
analysis, and again predict impacts for respondents.27 We then plot the distributions for these
two sets of estimates in Figure 1. For additional comparisons, we also plot a simulated
distribution of teacher effectiveness, which is simply a normal distribution with a standard
deviation of 0.10. This is approximates the variation in value-added among new teachers
estimated by Kane et al. (2006) for New York City teachers and serves as a simple benchmark
against which to measures the variation in predicted teacher effectiveness using the two factors.
Examining these plots, we see a clear increase in the variation of predicted teacher
effectiveness as we use the information from non-traditional credentials (Figure 1). The standard
deviation of predicted teacher effectiveness using only the traditional credentials to generate our
factor estimates is 0.021, and adding the non-traditional credentials raises the standard deviation
27 As we would expect, the coefficients in this additional regression are nearly identical (0.033 for cognitive skills
and 0.034 for non-cognitive). However, the variation in the factors decreases due to the smaller number of variables
used to make the factor estimates.
to 0.035.28 This suggests that districts may be able to gain some traction in selecting more
effective teachers by using broader sets of information during recruitment. However, the
variation of predicted value-added with an expanded set of data on new teachers has only about
12 percent of the variance of the expected distribution of teacher effectiveness. This underscores
the difficult, perhaps impossible, task of identifying systematically the most highly effective or
ineffective teachers without any data on actual performance in the classroom.
We use a survey of new teachers in New York City to investigate whether one can predict
economically significant variation in teacher effectiveness using broadened set of information on
new recruits. The evidence we present suggests that this is the case, and shows in particular that
predictive power is gained by using measures of teacher effectiveness suggested by earlier
research but rarely, if ever, collected and used by school districts.
Our findings are in a spirit similar to a recent paper by Boyd et al. (2008a) which makes
the argument that recruiting teachers with a number of attractive credentials while avoiding
teachers whose credentials are unattractive has potential power to improve the effectiveness of
their teacher workforce. Importantly, their results rely not on any single variable (e.g., teacher
certification pathway), but instead rely on a broad set of credentials, all of which are fairly
traditional indicators of teacher quality but some (e.g., SAT scores) are not currently collected by
many school districts, including New York City. Our results go further, and suggest collecting a
set of measures that would not appear on a teacher’s curriculum vitae.
28 The bimodal distribution of predicted effectiveness based on traditional characteristics is driven primarily by
higher predicted effectiveness of TFA corps members. Also, note that we might have plotted predictions of teacher
effectiveness using regressions that included all of the individual credentials as covariates. However, a large
number of variables capturing information on teachers would be able to explain some variation in student
achievement even if these variables were completely invalid predictors of teacher effectiveness. Indeed, using
Monte Carlo simulations, we find that random assignment of a large number of characteristics (e.g., 10 to 15)
generates substantial variance in “predicted effectiveness,” on the order of 0.06 to 0.08 standard deviations.
While our findings provide motivation for schools to expand the set of criteria used in
recruitment, there are a number of reasons why the results should be interpreted with caution.
First, our survey was completed well after the start of the school-year. Thus, teachers’
experiences during the school year may have affected some of their responses. For most survey
items, the problem of reverse causality is highly unlikely (e.g., reported SAT scores or cognitive
ability), but for others it may be potentially important (e.g., feelings on personal efficacy).
Second, the only way to truly validate our findings is to gather a similar set of information on a
new sample of teachers and test whether our results here are also found for this new sample.
Thus more work is necessary in this line of research.
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Table 1: Comparison of Teachers by Survey Invitation and Response
Test of Equality by
Number of Teachers 418 184 4,275
Teacher Absences 5.70 5.76 0.87 6.40
Mentor Rating Overall 0.04 -0.05 0.07 -0.01
Teacher Returned to NYC 91.9% 93.5% 0.49 92.6%
Teacher Returned to School 83.0% 85.3% 0.48 84.5%
Female 77.8% 66.3% 0.00 75.5%
Black 13.9% 17.4% 0.27 13.1%
Hispanic 8.6% 9.2% 0.80 11.6%
Asian 9.8% 9.2% 0.83 6.3%
Age 27.74 27.01 0.18 28.62
Traditionally Certified 48.8% 46.2% 0.56 51.5%
Teaching Fellow 29.2% 25.0% 0.29 31.3%
Teach for America Member 14.8% 22.3% 0.03 8.3%
Masters Degree 31.3% 25.5% 0.15 35.8%
Percent Black 34.1% 36.9% 0.25 35.6%
Percent Hispanic 47.9% 47.7% 0.95 45.0%
Percent Asian 9.7% 7.8% 0.17 9.0%
Pupil-Teacher Ratio 14.34 14.28 0.75 14.51
Percent Free Lunch 75.2% 75.3% 0.97 70.2%
Notes: Shown are the average values of each variable, broken down by whether a teacher was invited to take the survey and
whether they responded to the invitation. School characteristics are taken from the Common Core of Data. P-values are taken
from a test of the significance of an indicator for survey response in a regression that includes only those individuals who were
invited to take the survey.
Table 2: Summary Statistics on Survey Responses
Academic Background Observations Mean S.D.
Math/Science Major 403 20.6% 0.405
Education Major 403 14.6% 0.354
Has a Graduate Degree 402 32.1%
SAT Verbal Score 270 606.1 94.5
SAT Math Score 271 613.0 90.9
Barrons Rank of College (1 to 9 scale, 1 is best) 248 5.6 1.9
Passed the LAST Certification Exam on 1st Try 370 92.2%
Cognitive Ability (Percentile) 333 53.4 25.9
Math Knowledge for Teaching (Percent Correct) 337 0.57 0.20
Extraversion 396 3.60 0.66
Agreeableness 396 4.11 0.45
Conscientiousness 396 4.04 0.52
Emotional Stability 396 4.44 0.64
Open to New Experiences 396 3.85 0.53
Personal Efficacy 387 3.81 0.63
General Efficacy 387 3.19 0.79
Haberman PreScreener Performance
Haberman “Top Group” 338 21.3% 0.410
Haberman Total Correct 338 31.86 4.81
Table 3: Traditional Predictors of Teacher and Student Outcomes
Has a Graduate Degree -0.014 0.133 0.019 -0.050 -0.016
(0.024) (0.138) (0.412) (0.033) (0.035)
[0.557] [0.338] [0.962] [0.130] [0.649]
Passed LAST Certification Exam on 1st Attempt (1=yes) 0.035 0.123 0.013 -0.053 0.001
(0.039) (0.196) (0.688) (0.040) (0.059)
[0.369] [0.529] [0.984] [0.185] [0.982]
Teaching Fellow -0.046 -0.184 1.006 0.118 -0.016
(Relative to Traditionally Certified) (0.027)* (0.138) (0.514)* (0.031)** (0.040)
[0.085] [0.185] [0.050] [0.000] [0.695]
TFA Corps Member 0.044 -0.052 -0.501 0.128 0.090
(Relative to Traditionally Certified) (0.030) (0.140) (0.422) (0.035)** (0.035)**
[0.151] [0.710] [0.235] [0.000] [0.011]
Math or Science Major 0.040 -0.048 -1.212 -0.063 -0.007
(Relative to Those Other Than Math, Science, or Education) (0.031) (0.183) (0.529)** (0.049) (0.049)
[0.2] [0.795] [0.022] [0.201] [0.879]
Education Major -0.009 -0.117 -0.485 -0.097 0.041
(Relative to Those Other Than Math, Science, or Education) (0.033) (0.144) (0.489) (0.042)** (0.038)
[0.789] [0.417] [0.321] [0.022] [0.279]
Self-Reported SAT Math Score (s.d.=1) 0.012 0.004 -0.119 0.008 0.005
(0.015) (0.075) (0.207) (0.020) (0.015)
[0.41] [0.960] [0.564] [0.686] [0.715]
Self-Reported SAT Verbal Score (s.d.=1) -0.003 0.035 0.145 0.026 0.004
(0.014) (0.081) (0.228) (0.020) (0.022)
[0.829] [0.666] [0.524] [0.188] [0.853]
Barrons Rank of College (s.d.=1) 0.022 -0.212 0.059 0.027 -0.014
(0.012)* (0.087)** (0.217) (0.018) (0.022)
[0.076] [0.015] [0.786] [0.118] [0.015]
Control for Student/School Characteristics and Zip Code FE √ √√ √ √
Observations 247,903 3,030 4,858 4,877 4,516
Note: Each set of coefficients (separated by dotted lines) represent different regressions. See text for a full listing of the student
and school charateristics used as control variables. Standard errors (in parentheses) are clustered by school; p-values for each
coefficient are shown in brackets. * significant at 10%; ** significant at 5%;
Table 4: Non-Traditional Predictors of Teacher and Student Outcomes
Cognitive Ability (Percentile, s.d.=1) 0.016 0.066 -0.422 0.016 0.021
(0.012) (0.058) (0.227)* (0.016) (0.019)
[0.174] [0.254] [0.063] [0.315] [0.270]
Math Knowledge for Teaching (Percent Correct, s.d.=1) 0.028 0.014 -0.407 0.006 -0.011
(0.012)** (0.065) (0.208)* (0.014) (0.017)
[0.024] [0.828] [0.051] [0.659] [0.504]
Conscientiousness (s.d.=1) 0.011 0.188 0.185 -0.000 0.010
(0.011) (0.059)** (0.169) (0.013) (0.020)
[0.319] [0.001] [0.273] [0.982] [0.624]
Extraversion (s.d.=1) 0.007 0.216 0.086 0.000 0.022
(0.011) (0.062)** (0.189) (0.015) (0.017)
[0.519] [0.001] [0.650] [0.986] [0.201]
General Efficacy (s.d.=1) 0.017 0.019 -0.028 0.037 0.009
(0.012) (0.057) (0.192) (0.016)** (0.016)
[0.149] [0.736] [0.885] [0.024] [0.591]
Personal Efficacy (s.d.=1) 0.012 0.192 0.148 0.015 0.014
(0.011) (0.060)** (0.204) (0.013) (0.015)
[0.271] [0.001] [0.470] [0.280] [0.372]
Control for Student/School Characteristics and Zip Code FE √ √√ √ √
Observations 247,903 3,030 4,858 4,877 4,516
Note: Each set of coefficients (separated by dotted lines) represent different regressions. See text for a full listing of the student and school
charateristics used as control variables. Standard errors (in parentheses) are clustered by school; p-values for each coefficient are shown in
brackets. * significant at 10%; ** significant at 5%;
Table 5: Predictors of Performance on the Haberman Pre-Screener
Has a Graduate Degree 0.078 0.013
Passed LAST Certification Exam on 1st Attempt (1=yes) 0.167 0.352
Teaching Fellow 0.007 0.026
(Relative to Traditionally Certified) (0.061) (0.137)
TFA Corps Member -0.039 0.190
(Relative to Traditionally Certified) (0.074) (0.168)
Math or Science Major -0.094 -0.005
(Relative to Majors Other Than Math, Science, or Education) (0.068) (0.174)
Education Major 0.005 0.006
(Relative to Majors Other Than Math, Science, or Education) (0.061) (0.138)
Self-Reported SAT Verbal Score (s.d.=1) 0.050 0.175
Self-Reported SAT Math Score (s.d.=1) -0.018 0.057
Barrons Rank of College (s.d.=1) 0.029 0.069
Cognitive Ability (Percentile, s.d.=1) 0.017 0.255
Math Knowledge for Teaching (Percent Correct, s.d.=1) 0.049 0.198
Conscientiousness (s.d.=1) 0.052 0.026
Extraversion (s.d.=1) 0.020 0.084
General Efficacy (s.d.=1) 0.060 0.375
Personal Efficacy (s.d.=1) 0.076 0.226
Control for Student/School Characteristics √ √
Note: Each set of coefficients (separated by dotted lines) represent different regressions where the outcome variable is
regression on a single predictor or set of predictor variables. We use a probit regression to predict being in the top
group according to Haberman’s classification and report the mean marginal effect. We use least squares regressions to
predict the total score and report coefficients. Robust standard errors shown in parentheses. * significant at 10%; **
significant at 5%;
In Top Group
Table 6: Haberman PreScreener Performance and Teacher and Student Outcomes
Haberman Top Group 0.033 0.243 0.928 0.009 -0.064
(0.031) (0.175) (0.564) (0.035) (0.050)
[0.297] [0.167] [0.100] [0.793] [0.206]
Haberman Total Score (s.d.=1) 0.021 0.141 0.135 0.027 -0.040
(0.013) (0.065)** (0.230) (0.018) (0.020)**
[0.110] [0.029] [0.556] [0.125] [0.043]
Controls for School Charateristics and School Zip Code √ √√√√
Observations 244,235 2,970 4,754 4,773 4,421
Note: Each set of coefficients (separated by dotted lines) represent different regressions. All regressions include indicators for grades taught
(for teachers who can be linked to student data), school level (primary, middle, high school, or other) and highest grade, school zip code
fixed effects, and school level observable characteristics (percentage of students by gender, ethnicity, free lunch receipt, school eligibility for
Title I, and the pupil-teacher ratio.). Standard errors (in parentheses) are clustered by school. * significant at 10%; ** significant at 5%;
Math or Science Major 0.0413 -0.2703
Teaching Fellow 0.12 -0.4366
Teach for America 0.5732 0.2122
Passed LAST Certification Exam on 1st Attempt (1=yes) 0.2693 -0.0149
Barrons Rank of College (s.d.=1) 0.6043 -0.0845
Self-Reported SAT Math Score (s.d.=1) 0.6603 -0.15
Self-Reported SAT Verbal Score (s.d.=1) 0.6031 0.0182
Cognitive Ability (Percentile, s.d.=1) 0.5527 -0.0793
Math Knowledge for Teaching (Percent Correct, s.d.=1) 0.6441 -0.0091
Education Major -0.3422 0.234
Has a Graduate Degree -0.183 0.1301
Extraversion (s.d.=1) 0.0595 0.3655
Conscientiousness (s.d.=1) -0.1289 0.4398
Personal Efficacy (s.d.=1) -0.1154 0.518
General Efficacy (s.d.=1) 0.4752 0.367
Haberman Total Score (s.d.=1) 0.3029 0.3574
Notes: Factor loadings calculated using the pairwise item correlation matrix and applying a Promax rotation.
Table 7: Factor Analysis on Predictor Variables
Table 8: Using Factors as Predictors of Teacher and Student Outcomes
Factor 1: Cognitive Skills (s.d.=1) 0.033 0.025 -0.227 0.043 0.005
(0.011)** (0.065) (0.195) (0.016)** (0.015)
Factor 2: Non-Cognitive Skills (s.d.=1) 0.033 0.272 -0.026 0.009 0.031
(0.015)** (0.068)** (0.243) (0.017) (0.020)
F-Test: All Factors Equal Zero (p-value) 0.0023 0.00 0.51 0.03 0.30
Observations 247,903 3,030 4,858 4,877 4,516
Control for Student/School Characteristics and Zip Code FE √ √√√√
Notes: All regressions include grade level fixed effects, school zip code fixed effects, and student, class, and school level
observable characteristics (see text for a complete list). Standard errors (in parentheses) are clustered at the school level. *
significant at 10%; ** significant at 5%.
Figure 1: Recruitment Information and the Distribution of Predicted Value-Added
0 10 20 30 40
-.2 -.1 0 .1 .2
Predicted Teacher Effectiveness (Student Level Standard Deviations)
Simulated Value Added (s.d. = 0.1)
Traditional Credentials (s.d. = 0.021) Traditional & Non-Trad. (s.d. = 0.035)
Note: Kernel density plots are shown of “Simulated Value Added” is the kernel density plot of a randomly drawn
normally distributed variable with mean zero and standard deviation 0.10. The kernel density plots of predicted
value-added from two regressions of student test scores on a set of teacher characteristics and other controls. All
regressions include grade level fixed effects, school zip code fixed effects, and student, class, and school level
observable characteristics (see text for a complete list).
Appendix Figure 1:
Illustrative Item for Test of Cognitive Ability (Raven’s Progressive Matrices)
Note: This item is reproduced from Figure 1 in Raven (2000). It is not from any currently used form of the Raven’s
Progressive Matrices test; it only illustrates the format of the test items. The design in a box at the top of the figure
has a part missing, and test takers must select among the eight options below to complete the design. Although
Raven (2000) does not give the correct response to this item, we surmise that it is option 6.
Appendix Figure 2:
Example of a Question on the Math Content Knowledge Test
Imagine that you are working with your class on subtracting large numbers. Among your
students’ papers, you notice that some have displayed their work in the following ways:
Which of these students is using a method that could be used to subtract any two whole
numbers? (Select ONE answer.)
a) A only
b) B only
c) A and B
d) B and C
e) A, B, and C
Note: This item is taken from the Elementary Math section of the Math Content Knowledge Test. The correct