Teach For America National AmeriCorps 

Evaluation 

2017–18 and 2018–19 School Years

Robert Nathenson, Max Pardo, Sushmita Subedi, Chad Henry, Megha Joshi, Mark 

Lachowicz, Kyle Neering, Sarah Peko-Spicer, Marina Castro, Adrian Duran, Angela 

Su, Emily Baumann, and Cassandra Meyer

DECEMBER 2021 
16092_11/21

Teach For America National AmeriCorps 

Evaluation 

2017–18 and 2018–19 School Years

DECEMBER 2021 

Authors:

Robert Nathenson, Max Pardo, Sushmita Subedi, Chad Henry, Megha Joshi, Mark Lachowicz, 

Kyle Neering, Sarah Peko-Spicer, Marina Castro, Adrian Duran, Angela Su, Emily Baumann, and 

Cassandra Meyer

Acknowledgments

The authors would like to thank the AIR leaders who provided support and thought partnership: 

Dean Gerdeman, Ellie Fulbeck, Jordan Rickles, and Martyna Citkowicz.

AIR® Headquarters

1400 Crystal Drive, 10th Floor 

Arlington, VA 22202-3289

+1.202.403.5000 | AIR.ORG

Notice of Trademark: “American Institutes for Research” and “AIR” are registered trademarks. All other brand, product, or company 

names are trademarks or registered trademarks of their respective owners.

Copyright © 2021 American Institutes for Research®. All rights reserved. No part of this publication may be reproduced, distributed, or 

transmitted in any form or by any means, including photocopying, recording, website display, or other electronic or mechanical 

methods, without the prior written permission of the American Institutesfor Research. For permission requests, please use the 

Contact Us form on AIR.ORG. 
ii | AIR.ORG Teach For America National AmeriCorps Evaluation

Contents

Executive Summary......................................................................................................................... 1

Key Findings................................................................................................................................ 1

Introduction .................................................................................................................................... 2

Research Questions ........................................................................................................................ 2

Population of Interest................................................................................................................. 3

Sources of Data........................................................................................................................... 3

Methodology............................................................................................................................... 3

Who Was Included in the Study?.................................................................................................... 6

Main Findings................................................................................................................................ 13

Descriptive Findings.................................................................................................................. 13

National Test Performance Findings......................................................................................... 18

Study Limitations .......................................................................................................................... 22

Conclusion..................................................................................................................................... 22

References .................................................................................................................................... 24

Appendix A. Data Cleaning............................................................................................................ 26

Appendix B. Propensity Score Matching Procedure..................................................................... 29

Appendix C. Model Specification.................................................................................................. 31

Appendix D. Additional Data Details............................................................................................. 33
iii | AIR.ORG Teach For America National AmeriCorps Evaluation

Exhibits

Exhibit 1a. Number of TFA Teachers by Data Processing Stage (Reading)..................................... 8

Exhibit 1b. Number of TFA Teachers by Data Processing Stage (Mathematics) ............................ 8

Exhibit 2a. Teacher Analytic Sample Sizes by School Year, Region, and Group (Reading)............. 9

Exhibit 2b. Teacher Analytic Sample Sizes by School Year, Region, and Group 

(Mathematics)............................................................................................................................... 10

Exhibit 3a. Student Analytic Sample Sizes by School Year, Region, and Group (Reading)........... 11

Exhibit 3b. Student Analytic Sample Sizes by School Year, Region, and Group 

(Mathematics) ............................................................................................................................... 12

Exhibit 4a. Student Descriptive Characteristics from Pre-Matched Sample (TFA Versus 

Non-TFA), Reading (N=3,092,221)................................................................................................ 14

Exhibit 4b. Student Descriptive Characteristics from Pre-Matched Sample (TFA Versus 

Non-TFA), Mathematics (N=2,522,728) ........................................................................................ 15

Exhibit 5a. Teacher Descriptive Characteristics from Pre-Matched Sample (TFA Versus 

Non-TFA), Reading (N=51,640)..................................................................................................... 16

Exhibit 5b. Teacher Descriptive Characteristics from Pre-Matched Sample (TFA Versus 

Non-TFA), Mathematics (N=35,743)............................................................................................. 16

Exhibit 6a. School Descriptive Demographics from Pre-Matched Sample (TFA Versus 

Non-TFA), Reading (N=5,030) ....................................................................................................... 17

Exhibit 6b. School Descriptive Demographics from Pre-Matched Sample (TFA Versus 

Non-TFA), Mathematics (N=4,582) ............................................................................................... 17

Exhibit 7a. Meta-Analysis Results for TFA AmeriCorps Members/Novice Teachers, by 

Subject and School Year + ............................................................................................................. 20

Exhibit 7b. Meta-Analysis Results for TFA Alum/Experienced Teachers, by Subject and 

School Year + ................................................................................................................................. 21

Exhibit Appendix D1a. Teacher-Level Standardized Mean Differences Pre- and PostMatching, Reading ........................................................................................................................ 33

Exhibit Appendix D1b. Teacher-Level Standardized Mean Differences Pre- and PostMatching, Mathematics................................................................................................................ 34
iv | AIR.ORG Teach For America National AmeriCorps Evaluation

Exhibit D2a. Point Estimates (and Standard Errors) for TFA AmeriCorps Members 

Analysis by Region, Year, and TFA Status..................................................................................... 35

Exhibit D2b. Point Estimates and Standard Errors for TFA Alum Analysis by Region, Year, 

and TFA Status .............................................................................................................................. 35

Exhibit D3a. Student Descriptive Characteristics from Analytic Sample (TFA Versus NonTFA), Reading (N=40,757) ............................................................................................................. 36

Exhibit D3b. Student Descriptive Characteristics from Analytic Sample (TFA Versus NonTFA), Mathematics (N=42,514)..................................................................................................... 37

Exhibit D4a. Teacher Descriptive Characteristics from Analytic Sample (TFA Versus NonTFA), Reading (N=851) .................................................................................................................. 38

Exhibit D4b. Teacher Descriptive Characteristics from Analytic Sample (TFA Versus NonTFA), Mathematics (N=788).......................................................................................................... 38

Exhibit D5a. School Descriptive Demographics from Analytic Sample (TFA Versus NonTFA), Reading (N=563) .................................................................................................................. 39

Exhibit D5b. School Descriptive Demographics from Analytic Sample (TFA Versus NonTFA), Mathematics (N=499) .......................................................................................................... 39
1 | AIR.ORG Teach For America National AmeriCorps Evaluation

Executive Summary 

The American Institutes for Research® (AIR®) conducted a quasi-experimental evaluation of 

student reading and mathematics achievement in eight Teach For America (TFA) regions. Students 

who had a TFA AmeriCorps Member or alumnus as their primary teacher were compared to a 

matched sample of students who had a non-TFA teacher. For the analysis, the effect of 

receiving instruction from a TFA teacher is estimated relative to receiving instruction from a 

teacher with similar years of experience but who entered the teaching workforce through a 

non-TFA route (e.g., a university-based training program or alternative teacher preparation 

program). We examined performance on end-of-grade state assessments controlling for prioryear performance in two recent school years—spring of 2017–18 and spring of 2018–19—in 13 

sites within the following eight regions: (1) New York City, NY; (2) Camden, NJ; (3) CharlottePiedmont Triad (“CPT”), NC; (4) Eastern North Carolina (“ENC”), NC; (5) an urban district in the 

Mountain West Region; (6) Clark County, NV; (7) Bay Area, CA; and (8) Los Angeles Area, CA. 

TFA teachers were matched to non-TFA teachers through a propensity score matching model. 

The site-level data were then analyzed in a multi-level multivariate regression model with 

students nested within teachers. In order to synthesize results to estimate an overall effect, we 

first pooled results from the site-level to the region-level and then pooled results from the eight 

regions using a meta-analytic approach. Our two primary research questions were as follows: 

1. Across TFA regions, what was the impact of being taught by TFA AmeriCorps Members

(“ACMs”) on student academic achievement (reading and mathematics) as compared to 

being taught by novice non-TFA teachers?

2. Across TFA regions, what was the impact of being taught by TFA alumni (“Alum”) on student 

academic achievement (reading and mathematics) as compared to being taught by 

experienced non-TFA teachers?

Key Findings

• TFA ACMs – No statistically significant average differences for reading or mathematics

– Across all regions, being taught by a TFA ACM teacher did not result in significant 

average differences in student standardized state assessment reading or mathematics 

scores compared to being taught by a non-TFA novice teacher. This finding holds in both 

the 2017–18 and 2018–19 school years.

• TFA Alum – No statistically significant average differences for reading or mathematics

– Across all regions, the meta-analysis indicates that being taught by a TFA Alum teacher 

did not result in significant average differences in student reading or mathematics 

scores compared to being taught by a non-TFA experienced teacher in either the 2017–

18 or 2018–19 school years.
2 | AIR.ORG Teach For America National AmeriCorps Evaluation

Introduction 

For more than 25 years, Teach For America (TFA) has recruited and prepared aspiring leaders to 

teach in low-income and disadvantaged communities throughout the United States in order to 

help confront educational inequity through measurable student growth. With more than 

55,000 alumni and current members, TFA is one of the largest nationwide nontraditional routes 

into the teaching profession. Today, TFA continues its commitment of working toward 

excellence and equity for all by recruiting an increasingly diverse group of teachers.

Although prior research on TFA members has shown that students taught by TFA teachers 

perform similarly to or better than students in non-TFA classrooms (Antecol et al., 2013; Backes 

et al., 2016; Clark et al., 2013; Clark et al., 2017; Glazerman et al., 2006; Xu et al., 2011), new 

research is needed to better understand the impact of the more recent and diverse set of TFA 

members, as well as the continuous changes TFA has made to its recruitment, selection, and 

training models. Consequently, TFA has contracted with the American Institutes for Research 

(AIR) to conduct a rigorous evaluation of recent TFA-AmeriCorps Member cohorts to examine 

the impact of this more diverse set of members on student learning across multiple regions 

throughout the country. This report provides a large-scale examination as to how TFA 

AmeriCorps Members performed based on student reading and mathematics outcomes for the 

2017–18 and 2018–19 school years across eight TFA regions—(1) New York City, New York; (2) 

Camden, New Jersey; (3) Charlotte-Piedmont Triad, North Carolina and (4) Eastern North 

Carolina; (5) an urban district in the Mountain West Region1; (6) Clark County, Nevada; (7) Bay 

Area, California; and (8) Los Angeles Area, California. For this report, students who had a TFA 

AmeriCorps Member or alumnus as their primary teacher were compared to a matched sample 

of students who had a non-TFA teacher. For the analysis, the effect of receiving instruction 

from a TFA teacher is estimated relative to receiving instruction from a teacher with similar 

years of experience but who entered the teaching workforce through a non-TFA route (e.g., a 

university-based training program or alternative teacher preparation program).

Research Questions

This study addressed the following research questions: 

1 The Mountain West Region requested anonymity when reporting results.
3 | AIR.ORG Teach For America National AmeriCorps Evaluation

1. Across TFA regions, what was the impact of being taught by TFA AmeriCorps Members

(“ACMs”) on student academic achievement (reading and mathematics) as compared to 

being taught by novice non-TFA teachers?

2. Across TFA regions, what was the impact of being taught by TFA alumni (“Alum”) on student 

academic achievement (reading and mathematics) as compared to being taught by 

experienced non-TFA teachers?

Population of Interest

In this study we focus on achievement of students taught by TFA ACMs and Alum as compared 

to those taught by non-TFA novice and experienced teachers. We focus on students in grades 4-

8 as these students are required to take an end of year state assessment in reading and 

mathematics, and were required to do so in the previous year.2

Sources of Data

We used data from two sources for this study. First, lists of TFA ACMs and Alum in the 2017–18 

and 2018–19 school years in each of the TFA regions examined were provided by TFA. These 

lists also included the year a teacher started with TFA (their TFA start year). Second, studentand teacher-level data were largely provided by research departments of districts/charter 

management organizations (CMOs) within the eight TFA regions – 13 different sites in total.

3

Data received across districts/CMOs included student end-of-year state assessment outcome 

data, student demographic information, attendance rates, course enrollment, teacher 

demographic information, and student-teacher linkages through course enrollment. Staff at 

each site (i.e. district/CMO/NCERDC) matched the TFA ACMs and Alum lists to their database, 

flagging in each academic year whether a teacher was a TFA ACM, TFA Alum, or neither.

Methodology

Staff at AIR cleaned and analyzed the data received from each site. For a detailed discussion of 

the data-cleaning process, including how the primary teacher for each subject was identified, 

please see Appendix A. 

2 The only exception is Camden, for which high school students are also required to take state assessments in reading and 

mathematics. 3 Within the New York, Camden, Mountain West district, and Clark County TFA regions, information was provided by the 

eponymous district. In the CPT and ENC regions in North Carolina, information for all districts in both regions was provided by 

Duke University’s North Carolina Education Research Data Center (NCERDC). Information for the LA Area and Bay Area came 

from multiple districts/CMOs. The Los Angeles region included data from Los Angeles Unified School District (LAUSD), KIPP

SoCal, and Green Dot Public Schools. In the Bay Area region we received data from Oakland Unified School District, (OUSD) 

West Contra Costa School District (WCCSD), KIPP Northern California, and Aspire Public Schools. 
4 | AIR.ORG Teach For America National AmeriCorps Evaluation

Outcomes 

End-of-Grade State Assessment. For comparability, we standardized student end-of-grade 

state assessment scores by grade and year in reading and mathematics. In this way, the 

distributions of student performance are preserved and placed into a common metric. This 

measure of student performance is continuous.

Defining Novice and Experienced Teachers

As described earlier, the focus of this study is to evaluate the effect of receiving instruction 

from a TFA teacher relative to receiving instruction from a teacher with similar years of 

experience but who entered the teaching workforce through a non-TFA route (e.g., a universitybased training program or alternative teacher preparation program). We split years of 

experience into two groups, where novice teachers are defined as teachers with 1-2 years of 

experience and experienced teachers as those with at least 3 years of experience.

To compare TFA ACMs and TFA Alum to similarly experienced non-TFA teachers, we first had to 

identify a measure of years of experience. Most sites provided teacher’s years of experience in 

their school or district.

4 For TFA ACMs, we define years of experience by subtracting the year a 

teacher started with TFA (as provided by TFA) from the academic school year.5 This provided a 

time-varying measure of years of experience for TFA ACMs derived from TFAs records of their 

ACMs’ start year. For all non-TFA teachers and TFA Alum, we used each sites’ years of 

experience or salary step to define years of experience (salary step comes from a 

predetermined annual teacher salary table, where a teacher’s step, or level, is usually 

determined from a combination of years of experience / years of service and education 

credentials and credits). For further details on how years of experience was measured, please 

see Appendix A.

Propensity Score Matching

Because this evaluation focuses on the effect of receiving instruction from a TFA teacher 

relative to receiving instruction from a teacher with similar years of experience but who 

entered the teaching workforce through a non-TFA route, we match TFA ACMs to novice nonTFA teachers and TFA Alum to experienced non-TFA teachers. We employed a two-stage 

propensity score matching procedure (Rickles, 2014). This matching was conducted separately 

4 OUSD and WCCSD provided salary step instead. In OUSD, salary step represents the “years of service as a full-time certified 

teacher” 

(https://www.ousd.org/cms/lib/CA01001176/Centricity/Domain/105/OUSD%20Teacher%20Salary%20Scale%20Printable.pdf). 

In WCCSD, teachers “receive yearly step...increases commensurate with their teaching and education experience” 

(https://www.wccusd.net/cms/lib/CA01001466/Centricity/domain/53/salary%20schedules/2021-22/UTR%208-2021-22%20-

8.4.21.pdf). 5 Experience for TFA ACMs was calculated using the difference between the current school year and the TFA start year, 

increased by one. For OUSD, which didn’t include TFA start year. We used salary step instead.
5 | AIR.ORG Teach For America National AmeriCorps Evaluation

for reading and mathematics. For each subject, teachers were exact matched within specified 

grade bands (4-5, 6-8, 9-12) and teacher experience: novice teachers (<2 years) and 

experienced teachers, where experienced teachers were separated into two categories of those 

with 3-5 years of experience and those with at least 6 years of experience. Teachers were 

matched on their aggregate prior year student achievement, and the proportion of students 

that were students of color, female, special education, and English Learners (ELs). The 

procedure used nearest neighbor 1:1 matching with replacement and a caliper of 4.0. In the 

first stage, TFA teachers were matched to non-TFA teachers within the same school. For TFA 

teachers that did not match with a teacher in the same school, TFA teachers were matched to 

non-TFA teachers in different schools within a school cluster. School clusters were defined by 

comparability of school-average standardized prior year achievement scores. The average 

match rate was 80%, with a region minimum of 57% and maximum of 100%. This process 

resulted in 598 matched TFA teachers in reading and 627 in mathematics.

Estimation Method

Post-matching, the impact on student achievement for the group of students who were taught 

by TFA teachers as compared to the matched sample of students who were taught by non-TFA 

teachers represents the average treatment effect on the treated. To estimate this impact, we fit 

a two-level hierarchical linear model with students (Level 1) nested within teachers (Level 2). 

The model included a vector of individual student characteristics and a vector of teacher-level 

controls. Student characteristics included grade, race/ethnicity, sex, EL status indicator, special 

education status indicator, and free or reduced-price lunch eligibility status indicator. It also 

included an indicator for students who repeated a grade, an indicator for students who were 

transient (i.e., they were not enrolled for the full school year), and a measure of students’ 

attendance rate (defined as the proportion of enrolled school days in which they attended).6 At 

the teacher level, the model included teachers’ highest level of education, their number of 

students, and the proportion of their students who were the following: students of color, 

female, special education students, ELs, and eligible for free or reduced-price lunch. It also 

included aggregate prior year student achievement at the school level and school size.

7 We 

used robust standard errors. The model included weights that account for some TFA teachers 

being matched to the same comparison teachers (due to matching with replacement).

8 TFA 

ACMs were compared to non-TFA teachers with 2 or fewer years of experience. TFA Alum were 

compared to non-TFA teachers with at least 3 years of experience. We ran separate models for 

each of the 13 sites in each year (2017–18 and 2018–19), for each subject (reading and 

6 We note that not all pieces of information were provided in all sites.

7 School size was not included in the NYC analyses. 8 Weights and robust standard errors were not included in the Camden analyses due to small sample size.
6 | AIR.ORG Teach For America National AmeriCorps Evaluation

mathematics), and for each TFA category (TFA ACMs versus novice non-TFA and TFA Alum 

versus experienced non-TFA). In total, we estimated 104 regression specifications.

Meta-Analysis

We used two meta-analytic models to synthesize the results from 13 sites to estimate an

overall average treatment effect across 8 regions of having a TFA teacher on reading and 

mathematics student achievement in the 2017–18 and 2018–19 school years. (1) We first 

pooled results from the site-level to the region level in the LA and Bay Areas. (2) We then 

pooled results from the eight regions. To pool effects by region, we ran a univariate fixedeffects meta-regression model. To estimate an overall average across all regions, we ran a 

random-effects multi-level meta-regression model with districts nested within regions (Bloom 

et al., 2017; Hedges & Olkin, 2014; Hedges & Vevea, 1998).

Who Was Included in the Study? 

The analytic sample of TFA teachers included in this study was restricted to those that were 

linked by reading or mathematics subject to a student, that were identified as the primary 

teacher in that subject for that student, and had enough students (>5) to calculate aggregate 

student information. Non-TFA teachers were similarly restricted, and were only included if they 

were matched to a TFA teacher through the propensity score matching process described 

previously. Students were restricted to those without missing achievement score, prior year 

achievement score, and covariate data, and who were able to link to a primary reading or 

mathematics teacher.

Exhibits 1a and 1b provide further detail on how we derived the number of teachers included in 

the analytic sample for the reading and mathematics analyses. As described previously, sites 

matched the lists of ACMs and Alum provided by TFA to their teacher database. The first 

column (‘Original’) details the output of that match - the total number of teachers that were 

flagged as TFA within each region, by year and ACM/Alum status. For instance, 551 teachers in 

NYC in both Exhibits 1a and 1b. The next column reflects the number of teachers that were 

linked to students by subject (e.g. 531 in reading and 400 in mathematics in NYC). The following 

are those we identified as the student’s primary teacher, with the subsequent column teachers 

that had sufficient data on the variables needed to conduct the matching. The final column 

represents the number of TFA teachers that were successfully matched to non-TFA teachers. 

Overall 80% of the TFA teachers eligible for inclusion in the matching were matched to a nonTFA teacher. This final column often represents the set of TFA teachers that were included in 

the analysis, though some teachers in some regions were excluded post-matching if their 
7 | AIR.ORG Teach For America National AmeriCorps Evaluation

students were missing the information described above. We note that there were few 

differences between the pre-matched and analytic samples on student, teacher, and schoollevel characteristics (described further below).
8 | AIR.ORG Teach For America National AmeriCorps Evaluation

Exhibit 1a. Number of TFA Teachers by Data Processing Stage (Reading)9

Region Original 

(N=3434)10

Linked to 

Students

(N=2190) 

Primary Teacher

(N=1953) 

Sufficient 

Data

(N=758) 

Matched

(N=598) 

NYC, NY 551 531 379 160 121

Camden, NJ 3 3 3 3 3 

CPT, NC 491 299 283 120 119

ENC, NC 311 207 192 98 98

Clark County, NV 705 336 323 122 71

Bay Area, CA 768 350 322 165 129

LA Area, CA 605 464 451 90 57

Exhibit 1b. Number of TFA Teachers by Data Processing Stage (Mathematics)11

Region Original

(N=3434)12

Linked to 

Students

(N=1987) 

Primary Teacher

(N=1854) 

Sufficient 

Data

(N=764)

Matched

(N=627)

NYC, NY 551 400 296 119 85

Camden, NJ 3 N/A N/A N/A N/A

CPT, NC 491 324 317 189 189

ENC, NC 311 187 181 91 85

Clark County, NV 705 322 320 127 73

Bay Area, CA 768 319 308 162 144

LA Area, CA 605 435 432 76 51

9 Data for the Mountain West district was previously processed for a separate TFA report. 10 Combines all TFA teachers across subjects. 11 Data for the Mountain West district was previously processed for a separate TFA report. 12 Combines all TFA teachers across subjects. 
9 | AIR.ORG Teach For America National AmeriCorps Evaluation

Reading. The analytic sample for novice teachers (<2 years of teaching experience) included 

265 ACMs (see Exhibit 2a below) with 10,570 linked students (Exhibit 3a) and the comparison 

group of 211 non-TFA teachers with 8,072 linked students. The analytic sample for experienced 

teachers included 328 TFA Alum with 14,098 linked students and the comparison group of 287

non-TFA teachers with 11,239 linked students. 

Mathematics. The analytic sample for novice teachers (<2 years of teaching experience) 

included 293 ACMs (Exhibit 2b) with 12,214 (Exhibit 3b) linked students and the comparison 

group of 214 non-TFA teachers with 8,443 linked students. The analytic sample for experienced 

teachers included 293 TFA Alum with 13,993 linked students and the comparison group of 263

non-TFA teachers with 11,298 linked students.13

Exhibit 2a. Teacher Analytic Sample Sizes by School Year, Region, and Group (Reading) 

Region

Novice Teachers Experienced Teachers

TFA Teachers 

(ACMs) 

(N=265) 

Non-TFA Teachers

(N=211) 

TFA Teachers 

(Alum)

(N=328) 

Non-TFA Teachers

(N=287) 

2017–18

NYC, NY 12 11 52 49

Camden, NJ 1 1 1 1 

CPT, NC 28 25 18 18

ENC, NC 21 16 11 11

Mountain West 

district

17 17 10 7 

Clark County, NV 13 10 16 15

Bay Area, CA 27 16 35 27

LA Area, CA 7 7 20 15

2018–19

NYC, NY 13 11 44 42

Camden, NJ N/A14 N/A 1 1 

CPT, NC 32 26 21 21

13 Though the analytic sample sizes for reading and mathematics are reported separately by year and subject, a teacher could 

have appeared in multiple years or subjects. If so, they were counted as part of each sample. 14 There were no TFA ACMs in reading in Camden in 2018-19. 
10 | AIR.ORG Teach For America National AmeriCorps Evaluation

Region

Novice Teachers Experienced Teachers

TFA Teachers 

(ACMs) 

(N=265) 

Non-TFA Teachers

(N=211) 

TFA Teachers 

(Alum)

(N=328) 

Non-TFA Teachers

(N=287) 

ENC, NC 29 22 12 11

Mountain West 

district

17 16 8 4 

Clark County, NV 17 13 25 22

Bay Area, CA 22 12 39 31

LA Area, CA 9 8 15 12

Exhibit 2b. Teacher Analytic Sample Sizes by School Year, Region, and Group (Mathematics)

Region

Novice Teachers Experienced Teachers

TFA Teachers 

(ACMs) 

(N=293) 

Non-TFA Teachers

(N=214) 

TFA Teachers 

(Alum)

(N=293) 

Non-TFA Teachers

(N=263) 

2017–18

NYC, NY 7 5 42 40

Camden, NJ15 N/A N/A N/A N/A

CPT, NC 43 21 18 17

ENC, NC 25 19 6 6 

Mountain West 

district

22 22 5 5 

Clark County, NV 10 7 24 23

Bay Area, CA 22 16 42 30

LA Area, CA 6 6 13 14

2018–19

NYC, NY 8 7 28 27

Camden, NJ16 N/A N/A N/A N/A

15 There were no TFA ACMs in mathematics in Camden in 2017–18. 16 There were no TFA ACMs in mathematics in Camden in 2018–19. 
11 | AIR.ORG Teach For America National AmeriCorps Evaluation

Region

Novice Teachers Experienced Teachers

TFA Teachers 

(ACMs) 

(N=293) 

Non-TFA Teachers

(N=214) 

TFA Teachers 

(Alum)

(N=293) 

Non-TFA Teachers

(N=263) 

CPT, NC 52 33 24 25

ENC, NC 20 16 9 9 

Mountain West 

district

25 25 4 4 

Clark County, NV 16 14 23 20

Bay Area, CA 29 18 38 27

LA Area, CA 8 5 17 16

Exhibit 3a. Student Analytic Sample Sizes by School Year, Region, and Group (Reading) 

Region

Novice Teachers Experienced Teachers

Students of TFA 

Teachers (ACMs) 

(N= 10570) 

Students of NonTFA Teachers

(N= 8072) 

Students of TFA 

Teachers (Alum)

(N= 14098) 

Students of NonTFA Teachers

(N= 11239) 

2017–18

NYC, NY 211 337 1651 1414

Camden, NJ 5 20 18 29

CPT, NC 1602 1106 700 650

ENC, NC 1114 788 497 533

Mountain West 

district

435 534 330 213

Clark County, NV 681 406 510 498

Bay Area, CA 148 360 1259 842

LA Area, CA 1425 681 1703 1073

2018–19

NYC, NY 383 237 1333 1494

Camden, NJ N/A17 N/A 6 38

17 No students in the analytic sample as there were no TFA ACMs in reading in Camden in 2018–19. 
12 | AIR.ORG Teach For America National AmeriCorps Evaluation

Region

Novice Teachers Experienced Teachers

Students of TFA 

Teachers (ACMs) 

(N= 10570) 

Students of NonTFA Teachers

(N= 8072) 

Students of TFA 

Teachers (Alum)

(N= 14098) 

Students of NonTFA Teachers

(N= 11239) 

CPT, NC 913 925 1098 960

ENC, NC 1228 760 697 598

Mountain West 

district

555 810 274 71

Clark County, NV 566 360 1469 1154

Bay Area, CA 368 402 632 403

LA Area, CA 936 346 1921 1269

Exhibit 3b. Student Analytic Sample Sizes by School Year, Region, and Group (Mathematics) 

Region

Novice Teachers Experienced Teachers

Students of TFA 

Teachers (ACMs) 

(N= 12214) 

Students of NonTFA Teachers

(N= 8443) 

Students of TFA 

Teachers (Alum)

(N= 13993) 

Students of NonTFA Teachers

(N= 11298) 

2017–18

NYC, NY 174 134 1509 1175

Camden, NJ18 N/A N/A N/A N/A

CPT, NC 2060 809 936 920

ENC, NC 1268 706 306 361

Mountain West 

district

508 682 90 106

Clark County, NV 419 240 1386 1147

Bay Area, CA 65 278 539 464

LA Area, CA 844 856 2183 1186

2018–19

NYC, NY 178 216 1084 1094

18 No students in the analytic sample as there were no TFA ACMs in mathematics in Camden in 2017–18. 
13 | AIR.ORG Teach For America National AmeriCorps Evaluation

Region

Novice Teachers Experienced Teachers

Students of TFA 

Teachers (ACMs) 

(N= 12214) 

Students of NonTFA Teachers

(N= 8443) 

Students of TFA 

Teachers (Alum)

(N= 13993) 

Students of NonTFA Teachers

(N= 11298) 

Camden, NJ19 N/A N/A N/A N/A

CPT, NC 2792 1253 1210 1116

ENC, NC 849 642 433 453

Mountain West 

district

755 1102 49 142

Clark County, NV 738 524 1600 1177

Bay Area, CA 390 229 914 786

LA Area, CA 1174 772 1754 1171

Main Findings

This section includes a discussion of the findings of this report. First, to put in context the 

settings in which TFA teachers and non-TFA teachers work, we highlight descriptive differences 

in student characteristics in the pre-matched samples between TFA and non-TFA teachers. We 

also describe differences in teacher characteristics between the two groups, as well as 

aggregate school-level differences. We describe how these differences vary, if at all, between 

the pre-matched and analytic samples. Second, for the matched samples we discuss our main 

student achievement findings in the 2017–18 and 2018–19 school years across all eight regions.

Descriptive Findings

Student Level

Broadly, there are several differences in the demographic characteristics of students who had a 

TFA teacher and a non-TFA teacher in both reading and mathematics (see Exhibits 4a and 4b). 

Students taught by TFA teachers were more likely to be in middle school (Grades 6–8) than 

elementary school (Grades 3–5) as compared to students taught by non-TFA teachers. They 

tended to have lower prior year achievement, as measured by standardized state assessment 

scores. They were more racially/ethnically diverse—for reading, 29% of TFA taught students 

were African American compared to 22% for non-TFA taught students. Just over half (52%) of 

19 No students in the analytic sample as there were no TFA ACMs in mathematics in Camden in 2018–19. 
14 | AIR.ORG Teach For America National AmeriCorps Evaluation

TFA students were Hispanic compared to 46% for non-TFA teachers. The percentages were

similar for mathematics. Approximately 82% of reading and mathematics students of TFA ACMs

were eligible for free or reduced-price lunch, 4 percentage points higher than those of non-TFA 

teachers.

Exhibit 4a. Student Descriptive Characteristics from Pre-Matched Sample

(TFA Versus Non-TFA), Reading (N=3,092,221) 

Covariate TFA

(mean)

Non-TFA

(mean)

SMD SD

Prior Year Achievement -0.19 0.00 -0.19 1.00

Grade 4 0.06 0.12 -0.16 0.32

Grade 5 0.07 0.12 -0.14 0.32

Grade 6 0.13 0.10 0.11 0.30

Grade 7 0.13 0.10 0.10 0.30

Grade 8 0.14 0.10 0.14 0.30

White 0.10 0.18 -0.19 0.36

Black 0.29 0.22 0.18 0.39

Hispanic 0.52 0.46 0.13 0.47

Asian 0.06 0.11 -0.15 0.29

American Indian 0.01 0.01 -0.01 0.08

Native Hawaiian/Pacific Islander 0.00 0.00 0.00 0.04

Two or more races 0.02 0.03 -0.02 0.14

Race Not specified 0.00 0.00 -0.01 0.03

Female 0.48 0.48 -0.02 0.50

English Learner 0.16 0.16 0.01 0.36

Special education student 0.23 0.17 0.16 0.37

Free or reduced-price lunch eligibility 0.83 0.79 0.08 0.39

Student repeating current grade 0.03 0.04 -0.06 0.19

Transient 0.14 0.13 0.03 0.32

Proportion of school year attended 0.91 0.92 -0.08 0.12

SMD = standardized mean difference; SD = standard deviation
15 | AIR.ORG Teach For America National AmeriCorps Evaluation

Exhibit 4b. Student Descriptive Characteristics from Pre-Matched Sample

(TFA Versus Non-TFA), Mathematics (N=2,522,728) 

Covariate TFA

(mean)

Non-TFA

(mean)

SMD SD

Prior Year Achievement -0.19 0.00 -0.20 1.00

Grade 4 0.07 0.11 -0.14 0.31

Grade 5 0.07 0.11 -0.13 0.31

Grade 6 0.16 0.10 0.19 0.31

Grade 7 0.15 0.10 0.17 0.30

Grade 8 0.14 0.10 0.14 0.30

White 0.11 0.20 -0.21 0.38

Black 0.29 0.25 0.08 0.42

Hispanic 0.49 0.39 0.19 0.48

Asian 0.09 0.12 -0.11 0.31

American Indian 0.01 0.01 -0.01 0.09

Native Hawaiian/Pacific Islander 0.00 0.00 -0.01 0.04

Two or more races 0.02 0.03 -0.02 0.15

Race Not specified 0.00 0.00 -0.02 0.04

Female 0.48 0.48 -0.01 0.50

English Learner 0.20 0.16 0.09 0.36

Special education student 0.19 0.18 0.01 0.38

Free or reduced-price lunch eligibility 0.81 0.77 0.07 0.40

Student repeating current grade 0.03 0.04 -0.07 0.19

Transient 0.14 0.13 0.03 0.32

Proportion of school year attended 0.91 0.91 -0.07 0.13

SMD = standardized mean difference; SD = standard deviation
16 | AIR.ORG Teach For America National AmeriCorps Evaluation

Teacher Level

When averaged across teachers, more than 91% of TFA teachers’ students were students of 

color, which was at least 8 percentage points higher than that of non-TFA teachers’ set of 

students (Exhibits 5a and 5b). For those for which we had data, at least 83% of TFA students 

were eligible for free or reduced-price lunch, 4-5 percentage points higher than for non-TFA 

teachers. TFA teachers taught slightly more students than non-TFA teachers on average. 

Exhibit 5a. Teacher Descriptive Characteristics from Pre-Matched Sample

(TFA Versus Non-TFA), Reading (N=51,640) 

Covariate TFA

(mean)

Non-TFA

(mean)

SMD SD

Average Prior Year Student Achievement -0.50 -0.32 -0.23 0.83

Bachelor’s degree 0.88 0.82 0.11 0.23

Some postbaccalaureate 0.00 0.00 -0.02 0.02

Master’s degree 0.12 0.18 -0.10 0.23

Doctorate 0.00 0.00 -0.01 0.05

Students of color 0.92 0.84 0.38 0.20

Female students 0.42 0.44 -0.12 0.18

Special education students 0.41 0.34 0.17 0.37

EL students 0.22 0.21 0.07 0.26

Students eligible for free or reduced-price lunch 0.86 0.81 0.21 0.21

Number of students 40 38 0 48

SMD = standardized mean difference; SD = standard deviation

Exhibit 5b. Teacher Descriptive Characteristics from Pre-Matched Sample

(TFA Versus Non-TFA), Mathematics (N=35,743) 

Covariate TFA

(mean)

Non-TFA

(mean)

SMD SD

Average Prior Year Student Achievement -0.38 -0.29 -0.12 0.82

Bachelor’s degree 0.97 0.90 0.14 0.13

Some postbaccalaureate 0.00 0.00 -0.02 0.02

Master’s degree 0.03 0.09 -0.11 0.13

Doctorate 0.00 0.00 -0.02 0.02

Students of color 0.91 0.81 0.43 0.21

Female students 0.45 0.44 0.08 0.18

Special education students 0.34 0.37 -0.10 0.37
17 | AIR.ORG Teach For America National AmeriCorps Evaluation

EL students 0.22 0.19 0.15 0.21

Students eligible for free or reduced-price lunch 0.83 0.79 0.20 0.20

Number of students 49 44 0 43

SMD = standardized mean difference; SD = standard deviation

School Level

At the school level (Exhibits 6a and 6b), TFA ACMs taught in schools, on average, that served a 

higher proportion of students of color than those of non-TFA teachers. A greater share of the 

student bodies of these schools were also from low-income households, with about four-fifths 

eligible for free or reduced-price lunch, 5% percentage points higher than that of non-TFA 

teachers.

Exhibit 6a. School Descriptive Demographics from Pre-Matched Sample (TFA Versus NonTFA), Reading (N=5,030) 

Covariate TFA

(mean)

Non-TFA

(mean)

SMD SD

Students of color 0.89 0.82 0.36 0.18

Female students 0.49 0.48 0.10 0.07

Special education students 0.18 0.19 -0.04 0.12

EL students 0.17 0.16 0.10 0.14

Students eligible for free or reduced-price lunch 0.81 0.76 0.28 0.18

Number of students 722 720 0 546

SMD = standardized mean difference; SD = standard deviation

Exhibit 6b. School Descriptive Demographics from Pre-Matched Sample (TFA Versus NonTFA), Mathematics (N=4,582) 

Covariate TFA Non-TFA SMD SD

Students of color 0.87 0.80 0.36 0.19

Female students 0.48 0.48 -0.02 0.07

Special education students 0.18 0.19 -0.05 0.11

EL students 0.18 0.16 0.12 0.14

Students eligible for free or reduced-price lunch 0.77 0.72 0.26 0.18

Number of students 730 734 0 554

SMD = standardized mean difference; SD = standard deviation
18 | AIR.ORG Teach For America National AmeriCorps Evaluation

We also compared the pre-matched (Tables 4a-6b above) and analytic samples (Appendix 

Exhibits D3a-D5b) to examine whether there were any differences between the two samples. At 

the student-level, the difference in average prior year achievement between TFA and non-TFA 

teachers in the pre-matched sample is eliminated in the reading analytic sample and greatly 

reduced in the mathematics analytic sample. In addition, a smaller share of students with a TFA 

teacher was designated as special education in the analytic sample. In mathematics, the share 

of EL students with a TFA teacher increased slightly. All other student characteristics were 

within 3 percentage points between the TFA pre-matched and analytic samples. At the teacher 

level, the gap in teachers’ average prior year achievement of their students between TFA and 

non-TFA teachers is greatly reduced between the pre-matched and analytic samples. This, 

placed with the similar reduction at the student-level, indicates that the matching did a good 

job of creating a sample of TFA and non-TFA teachers whose students had similar prior year 

achievement. For further detail on prior year achievement in the pre- and post-matched 

samples, please see Appendix Exhibits D1a and D1b. Additionally, the average proportion of 

TFA teachers’ students that were female increased to 48% in the analytic sample, closely 

paralleling the percentage for non-TFA teachers. The proportion of TFA teachers with a 

Master’s degree decreased in the reading analytic sample and increased in the mathematics 

analytic sample as compared to the pre-matched samples. At the school-level, there were 

fewer students, on average, at schools in the analytic as compared to the pre-matched samples. 

There was also a slightly higher share of students designated as EL. All other school 

characteristics were within 3 percentage points between the pre-matched and analytic 

samples.

National Test Performance Findings

TFA ACMs – No statistically significant average differences for reading or mathematics

Exhibit 7a details the estimated average treatment effect of standardized reading and 

mathematics achievement for the group of students taught by TFA ACMs as compared to those 

taught by the matched sample of novice non-TFA teachers. The last row of the exhibit details 

the eight region aggregated average treatment effect from the meta-analytic models. All other

rows represent the region-specific treatment effect. Dots represent point estimates and lines 

represent 95% confidence intervals. Colors indicate whether results are from the 2017–18 

school year (green) or the 2018–19 school year (orange).

Overall, the meta-analytic average treatment effects for reading and mathematics in the 2017–

18 and 2018–19 school years were not statistically significant for students taught by TFA ACMs. 

While there is some variation across regions, the overall effect indicates that, on average, being 

taught by TFA ACMs did not result in a statistically significant difference in standardized 

achievement as compared to being taught by non-TFA novice teachers.
19 | AIR.ORG Teach For America National AmeriCorps Evaluation

TFA Alum – No statistically significant average differences for reading or mathematics

Exhibit 7b details the average treatment effect in reading and mathematics for the group of 

students who were taught by TFA Alum teachers as compared to those that were taught by the 

matched sample of experienced non-TFA teachers. The layout of the results by region and 

overall is the same as in Exhibit 7a.

The results for TFA Alum are similar to the findings for TFA ACMs. The meta-analytic average 

treatment effect for TFA Alum is not significantly different in either year in either subject. That 

is, there is no statistically significant difference in the overall average treatment effect for 

reading or mathematics in either year for the group of students who were taught by TFA Alum 

as compared to the group taught by non-TFA experienced teachers. For further detail on these 

estimates, please see Appendix D2a (reading) and D2b (mathematics).
20 | AIR.ORG Teach For America National AmeriCorps Evaluation

Exhibit 7a. Meta-Analysis Results for TFA AmeriCorps Members/Novice Teachers, by Subject and School Year + 

+ In the analytic sample in Camden, New Jersey, there were no TFA ACMs in reading in 2018–19 or mathematics in either year. 
21 | AIR.ORG Teach For America National AmeriCorps Evaluation

Exhibit 7b. Meta-Analysis Results for TFA Alum/Experienced Teachers, by Subject and School Year + 

+ In the analytic sample in Camden, New Jersey, there were no TFA Alum teachers in mathematics in either year.
22 | AIR.ORG Teach For America National AmeriCorps Evaluation

Study Limitations 

The results presented in this report have several limitations. First, propensity score matching, 

which matched TFA to non-TFA teachers on observed information only, cannot account for the 

potential of teacher self-selection into TFA or other teacher preparation programs. In addition, 

we were often limited by small sample sizes with respect to the amount of information that 

could be included in the matching. Second, in some instances, the propensity score matching 

resulted in standardized mean differences between TFA and non-TFA teachers that were

greater than the What Works Clearinghouse guideline of .25. For further detail on how the 

matching reduced differences on teachers’ aggregated student prior year achievement, please 

see Appendix Exhibits D1a and D1b. Third, the analytic sample was restricted to a subset of the 

overall TFA population based on a number of factors described previously. As discussed earlier, 

we observed a few differences between the pre-matched and analytic samples, including school 

size, share of students that had a special education designation, share of students designated as 

EL, and proportion of teachers with a Master’s degree. 

Fourth, the way years of experience was operationalized varied across sites. Most sites provided a 

teacher’s years of experience in the school or district, while two sites provided salary step 

instead. In each year, there were several instances of the TFA ACM/Alum designation not aligning 

with the years of experience identified. As such, TFA ACMs’ years of experience were top coded 

at 2 and TFA Alum’s years of experience bottom-coded at 3 to reflect the TFA ACMs and Alum

definitions. Additionally, the TFA ACM measure was typically calculated by subtracting the year 

the teacher started with TFA (provided by TFA) from the school year. We consider this calculation

as the most accurate measure available for years of experience for TFA ACMs. However, TFA 

Alum and non-TFA years of experience was operationalized by information provided by each site.

The limitation of having imperfect years of experience data is that, despite these efforts, in some 

instances we may not be comparing TFA ACMs to novice non-TFA and TFA Alum to experienced 

non-TFA teachers.

Conclusion

In this study, we examined reading and mathematics achievement of Grades 4–8 students in 

the 2017–18 and 2018–19 school years in eight regions across the United States who had a TFA 

ACM or Alumnus as their primary teacher as compared to those who had a non-TFA teacher. 
23 | AIR.ORG Teach For America National AmeriCorps Evaluation

In these settings, the descriptive analysis shows how in recent years TFA ACMs and Alum taught 

a greater proportion of students of color, students from low-income households, and students 

who had lower prior year achievement scores. TFA ACMs were located in schools that, as a 

whole, served more students from low-income households and students of color than non-TFA 

teachers.

For the matched sample, findings suggest no overall differences in student achievement across 

TFA and non-TFA teachers, as measured by the state standardized assessment score. Although 

there is some variation across regions, there is no discernible pattern to these findings across 

TFA ACMs or Alum, region, or year.

While these results do not suggest TFA teachers in recent years have outperformed non-TFA 

teachers, on average, on students’ standardized state assessments, the results do support the 

notion that school districts can expand and diversify their teacher workforce through TFA 

without significantly affecting student performance.
24 | AIR.ORG Teach For America National AmeriCorps Evaluation

References

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of student achievement in primary school: Evidence from a randomized experiment. 

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Backes, B., Hansen, M., Xu, Z., & Brady, V. (2016). Examining spillover effects from Teach For

America Corps members in Miami-Dade County public schools. Journal of Teacher 

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to study cross-site variation in treatment effects: A hybrid approach with fixed 

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The effectiveness of secondary math teachers from Teach For America and the Teaching 

Fellows Programs (NCEE 2013-4015). National Center for Education Evaluation and 

Regional Assistance, Institute of Education Sciences, U.S. Department of Education.

Clark, M. A., Isenberg, E., Liu, A. Y., Makowsky, L., & Zukiewicz, M. (2017). Impacts of the Teach 

For America Investing in Innovation scale-up. Mathematica Policy Research.

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Teach For America on student achievement and other outcomes. Journal of Policy 

Analysis and Management: The Journal of the Association for Public Policy Analysis and 

Management, 25(1), 75–96.

Hedges, L. V., & Olkin, I. (2014). Statistical methods for meta-analysis. Academic press.

Hedges, L. V., & Vevea, J. L. (1998). Fixed-and random-effects models in metaanalysis. Psychological methods, 3(4), 486.

Konstantopoulos, S., Hedges, L. V., Cooper, H., & Valentine, J. C. (2019). Statistically analyzing 

effect sizes: Fixed-and random-effects models. The handbook of research synthesis and 

meta-analysis, 245-280.

Rickles JH, Seltzer M. (2014) A Two-Stage Propensity Score Matching Strategy for Treatment 

Effect Estimation in a Multisite Observational Study. Journal of Educational and 

Behavioral Statistics, 39(6), 612-636. doi:10.3102/1076998614559748
25 | AIR.ORG Teach For America National AmeriCorps Evaluation

Xu, Z., Hannaway, J., & Taylor, C. (2011). Making a difference? The effects of Teach For America 

in high school. Journal of Policy Analysis and Management, 30(3), 447–469.
26 | AIR.ORG Teach For America National AmeriCorps Evaluation

Appendix A. Data Cleaning

We describe our data-cleaning procedure in this appendix.

20 Students were assigned a single 

race/ethnicity and sex. In instances in which a student’s race/ethnicity or sex designation in the 

data changed across years, the designation in the most recent year available was used. Student 

eligibility for free or reduced-price lunch, special education status, and EL status were 

determined by whether the data indicated that the student ever received those services. 

Students were also assigned a single grade level per year. Students with multiple grade levels in 

a single year were assigned to the most recently enrolled grade. When the grade selected for 

each student matched the grade selected the previous year, the student was designated as a 

grade repeater. 

Achievement outcomes were limited to a single score per subject, test, and school year. For 

instances in which students had multiple scores for the same outcome, the highest score was 

selected. Students who were enrolled in the district for fewer days than the number of days 

offered (the median value of all students by school and year) were considered transient. 

Attendance rate was calculated using the number of days attended divided by the number of 

days enrolled. 

Students were assigned one (primary) teacher for each subject. In cases in which students 

attended multiple courses in one subject, a series of rules determined which teacher would be 

selected as the primary teacher. Each of the following rules were implemented until a single 

teacher was selected: 

1. Course withdrawal date was used to determine the most recent course attended by the 

student for the school year.

2. Course entry and withdrawal date were used to calculate which course the student 

attended the longest. 

3. The number of students who took the course was used to determine which course was the 

most common. 

4. As a final tiebreaker, we used the first course in the data file, assigned before any data 

manipulation. 

20 Data for the Mountain Region was cleaned for a different TFA project and only reanalyzed to provide separate estimates for 

the 2017-18 and 2018-19 years.
27 | AIR.ORG Teach For America National AmeriCorps Evaluation

For instances in which multiple teachers were assigned the same course for the same duration

for the same student, the teacher connected with more students overall and with a greater 

number of years of experience was selected. 

Teacher race/ethnicity and sex followed the same rules as the student variables, with only a 

single designation across all years. In cases in which teacher education level was missing, the 

teacher was coded as having a bachelor’s degree. Teachers were assigned a single school per 

subject. In cases in which a teacher was the primary teacher for students across multiple 

schools, the most common school was selected. 

In addition to the process described earlier for measuring years of experience, we conducted 

the following data cleaning steps for years of experience. We ensured that years of experience 

was time-varying and that TFA status was time-varying, with TFA teachers switching from TFA 

ACM to TFA Alum status after year 2. We coded teachers in their first year as having 1 year of 

experience, in their 2nd year as 2 years, and so on.21 We do so as we measured student 

achievement in the spring of each academic year, when a teacher was nearing completion of 

the school year. We rounded all years of experience to whole numbers when necessary (e.g. 

NYC provided partial years of experience). For all TFA ACMs in a given year, we top coded their 

years of experience at 2. For all TFA Alum in a given year, we bottom coded their years of 

experience at 3. 

To validate the operationalization of TFA ACMs experience, across a variety of sites we 

correlated this measure as compared to the years of experience measure and teachers’ age 

provided by sites. The correlation was usually high. For instance, the correlation between 

district years of experience and TFA ACMs operationalized years of experience was .98 in Green 

Dot, .95 in CCSD, .95 in LAUSD, over .99 in Camden, and over .99 in NYC.22 It was lower in 

WCCSD at .53, which provided salary step instead. In some sites we examined teacher age in 

addition to years of experience – the correlation was .86 between age and operationalized TFA 

ACMs years of experience in Aspire. The correlations were also lower in North Carolina and so 

in both North Carolina regions we included an additional restriction when operationalizing TFA 

ACMs and Alum years of experience.23

21 Therefore, sites that assigned zero years of experience to new teachers had the years of experience increased by one for all 

teachers.

22 After removing three outlier cases in NYC.

23 To validate years of experience in North Carolina, we compared the provided measure for TFA ACM and Alum 

with our calculated years of experience measure. For TFA ACMs, we operationalized years of experience by 

subtracting the year a teacher started with TFA (as provided by TFA) from the academic school year. For TFA Alum 

and non-TFA teachers, we calculated years of experience by subtracting the year a teacher graduated with their 

highest degree (provided in the NCERDC data) from the academic school year. The correlation between North 
28 | AIR.ORG Teach For America National AmeriCorps Evaluation

Indicators for ACM and Alum TFA status were mutually exclusive by year. Additionally, teachers 

could never be indicated as non-TFA regardless of year if they were ever flagged as such. In 

cases in which the data provided did not meet these requirements, the TFA indicators were 

recoded using TFA start year. 

Carolina’s and our years of experience measures for TFA ranged between .64 and .69 for TFA ACMs (reading and 

mathematics, respectively), .39 and .64 for TFA Alum, and .73 for non-TFA. Because the measures were not highly 

correlated (often defined as a correlation of .8 or above), we imposed an additional restriction for defining years of 

experience. To be included in the analytic sample, novice teachers (defined as those with 2 or fewer years of 

experience) had to be listed as 0–2 years of experience on both measures. Experienced teachers were those with at 

least 3 years of experience.

We separated experienced teachers into two categories—those with 3–5 years of experience and those 

with at least 6 years of experience. For both the 3–5 and 6+ categories of experienced teachers to be included in 

the analytic sample, we imposed the same restriction—teachers had to fall within the appropriate category under 

both measures. While this restriction reduced the number of teachers included in the analyses, given the lower 

correlation between the 2 years of experience measures, it increased the validity of how the years of experience 

measure was defined. This factor is of critical importance in a study of this nature, which seeks to compare student 

performance across categories of teachers with very different paths into the teaching profession.
29 | AIR.ORG Teach For America National AmeriCorps Evaluation

Appendix B. Propensity Score Matching Procedure

We employed a two-stage propensity matching procedure (Rickles, 2014). This matching was 

conducted separately for reading and mathematics. For each subject, teachers were exact 

matched within specified grade bands (4-5, 6-8, 9-12) and teacher experience: novice teachers 

(<2 years) and experienced teachers, where experienced teachers were separated into two 

categories of those with 3-5 years of experience and those with at least 6 years of experience. 

Teachers were matched on the teacher-level variables described previously, including 

aggregate prior year student achievement, and the proportion of students that were students 

of color, female, special education, and EL. The procedure used nearest neighbor 1:1 matching 

with replacement and a caliper of 4.0. In the first stage, TFA teachers were matched to non-TFA 

teachers within the same school. For TFA teachers that did not match within their school, TFA 

teachers were matched to non-TFA teachers in different schools within a school cluster. School 

clusters were defined by comparability of school-average standardized prior year test scores. 

The total number of school clusters was determined by the number of schools in a district, 

where districts with less than 12 schools had a single school cluster, districts with 13 to 19 

schools had 3 school clusters, and districts with 20 or more schools had 5 school clusters. Cutoff 

values to determine cluster membership were terciles of the school-average prior year test 

score distribution for districts with 3 clusters, and quintiles for districts with 5 clusters (there 

were no cluster cutoffs for districts with a single cluster). The average match rate was 80%, with 

a region minimum of 57% and maximum of 100%. 

Propensity scores were estimated as follows. The estimation procedure was conducted 

separately for teacher subject (math and ELA) and school year (2018 and 2019). Under the logit 

function, g, let pij represent the probability teacher i is TFA in school j and llllll � ppiiii

1−ppiiii� the log 

odds. The probability of each outcome can be specified as a linear combination of the log odds of 

the logit function. Under the below specification, ββ1 represents the parameter for a vector of 

teacher-average variables (X), including percentage of minority students (Black, Asian, Latinx, 

other), percentage of female students, percentage of special education students, percentage of 

EL students, and average prior year standardized test score. School random effects uu0jj were

included to account for potential dependency of propensities of students within schools, where 

uu0jj~NN(0, ττ2). 

ll�ppiijj� = llllllllll�ppiijj� = llllll � ppiiii

1−ppiiii� = ββ0 + ββ1(XX)iiii + uu0jj + εεiijj (1)

We used Bayesian generalized mixed models to estimate propensity scores because the 

specification of prior distributions resulted in more stable estimation, particularly in regions 
30 | AIR.ORG Teach For America National AmeriCorps Evaluation

with relatively few schools (<10). Weak prior distributions were specified for the intercept ββ0, 

regression coefficients ββ1, and the between-school random effect variance ττ2. The matching 

procedure was conducted using the R package MatchIt (version 3.0.2). 
31 | AIR.ORG Teach For America National AmeriCorps Evaluation

Appendix C. Model Specification

Region Analyses 

To estimate the effect of having a TFA teacher, we separately analyzed 108 samples: two school 

years (2017–18 and 2018–19) for two TFA categories (TFA ACMs versus novice non-TFA and TFA 

Alum versus experienced non-TFA) for two subjects (reading and mathematics) at 13 sites 

across 8 regions. For each sample of matched TFA and non-TFA teachers, we specified a twolevel (students nested within teachers) hierarchical mixed-effects model with random 

intercepts at the teacher level. At Level 1, yyiijj represented the outcome of interest for state 

standardized assessment score. The key parameter of interest was the TFA indicator (0 = not 

TFA, 1 = TFA). The analyses of novice teachers were limited to those with 0–2 years of 

experience, and the comparison was between novice non-TFA teachers (0) and TFA ACMs (1). In 

the analyses of experienced teachers, the comparison was between experienced non-TFA 

teachers (0) and TFA Alum (1). It was limited to teachers with 3+ years of experience. The 

analyses included XXii, a vector of student characteristics, including grade indicators; 

race/ethnicity (binary indicators for Black, Asian, Hispanic, American Indian/Alaskan Native, 

Native Hawaiian or Other Pacific Islander, and Two or More Race/Ethnicities, with White 

students as the omitted category); sex (0 male, 1 female); a continuous attendance rate 

measure ([0,1]); and binary indicators for special education, English Learner, eligibility for freeor reduced-price lunch, repeating a grade, and whether the student was enrolled in the school 

for only part of the school year. TTiijj is a vector of teacher-level characteristics, including 

aggregated prior year achievement of the teacher’s students, teacher’s highest level of 

education (binary indicators for some post-college education, master’s, and doctorate/JD, with 

bachelor’s degree the omitted category), and a set of continuous measures aggregated from 

the student level, including proportion of a teacher’s students that were students of color, 

female, special education students, English Learners, and eligible for free or reduced-price 

lunch. It also included a count of the number of students each teacher taught. SSiijj is a vector of 

school-level information aggregated from all students in the school, including the school-level 

aggregate student prior year achievement and a count of the number of students in the school. 

We used robust standard errors. The residual was partitioned into the student-level 

component, rriijj and the teacher-level component, uu0jj, which were both assumed to be 

multivariate normal. M1 below specifies the modeling approach. 

yyiijj = ββ0+ββ1XXii + rriijj (M1 Level 1- Student Level)

ββ0jj = γγ00 + γγ01TTTTTT1jj + γγ02TT2jj + γγ03SS3jj + uu0jj (M1 Level 2 – Teacher Level)

ββ1jj = γγ10
32 | AIR.ORG Teach For America National AmeriCorps Evaluation

Meta Analysis 

We conducted propensity score matching within districts and estimated average treatment 

effects separately in each of the districts. Essentially, we emulated a multisite randomized 

experimental design and estimated site-specific average treatment effect estimates. We then 

used meta-analytic models to synthesize the results from the 13 sites to estimate an overall 

average treatment effect. 

To pool effects by region, we ran a univariate fixed-effects meta-regression model. Below let rr

denote region rr, denote site , where = 1 ... , mmrr total sites in region rr, and ββ

�ıı denotes the 

estimated average treatment effect from site in region rr. For each region the effects were 

pooled as follows (Konstantopoulos & Hedges, 2019). 

μμ�rr = 

∑ wwii ββ

�ıı ,mmrr

ii=1

∑ wwii mmrr

ii=1

Here, μμ�rr is the estimated average treatment effect for region rr. The fixed effects weights in 

meta-analysis are as follows. Below let σσii

2 denote the variance associated with the estimated 

average effect in site in region rr. 

wwii = 

1

σσıı

�2

To estimate an overall average across all regions, we ran a random-effects multi-level metaregression model with sites nested within regions (Bloom et al., 2017; Hedges & Olkin, 2014; 

Hedges & Vevea, 1998). Below, let μμ denote the overall average effect. The multi-level random 

effects meta-analytic model is as follows:

ββ

�ıı = μμ + uurr + vvii + eeii

Here, uujj ~ NN(0, ττ2) is the deviation between each effect parameter and the population 

average, vvii ~ NN(0, ωω2) is the deviation between each effect parameter and the region-level 

average effect, and eeii ~ NN(0, σσii

2 ) is the sampling error for effect in region rr. The randomeffects weights account for between-study variance (ττ2), within-study variance (ωω2), and 

sampling error (σσii

2 ). 
33 | AIR.ORG Teach For America National AmeriCorps Evaluation

Appendix D. Additional Data Details 

The plots below show standardized mean differences (SMDs) on students’ prior year 

achievement scores aggregated to the teacher level. The red dots represent the SMDs 

calculated on the sample before matching TFA to non-TFA teachers, and the blue dots 

represent the SMDs calculated on the sample post-matching. The y-axis contains different 

regions and the x-axis contains the SMDs. The dashed vertical line on x=0 represents absolute 

balance. For both reading and mathematics, across all regions, the SMDs are closer to 0 postmatching than pre-matching, indicating that matching resulted in better balance on the 

teacher-level prior year achievement scores. 

Exhibit Appendix D1a. Teacher-Level Standardized Mean Differences Pre- and Post-Matching, 

Reading 
34 | AIR.ORG Teach For America National AmeriCorps Evaluation

Exhibit Appendix D1b. Teacher-Level Standardized Mean Differences Pre- and Post-Matching, 

Mathematics
35 | AIR.ORG Teach For America National AmeriCorps Evaluation

Exhibit D2a. Point Estimates (and Standard Errors) for TFA AmeriCorps Members Analysis by 

Region, Year, and TFA Status

Subject

2017–18 2018–19

Reading Mathematics Reading Mathematics

Reading Overall .04 (.05) .39 (.36) -.03 (.07) -.15 (.13)

NYC, NY -.07 (.07) .15 (.36) .13 (.09) .31 (.16)

Camden, NJ -.54 (.43) N/A N/A N/A

CPT, NC .02 (.02) .02 (.02) -.01 (.01) .07 (.04)

ENC, NC -.02 (.05) -.01 (.02) .00 (.01) -.02 (.04)

Mountain West 

district

-.02 (.07) .04 (.08) .02 (.05) .09 (.04)

Clark County, NV .01 (.02) .07 (.11) .25 (.10) -.11 (.10)

Bay Area, CA .01 (.05) -.25 (.00) .10 (.02) -.28 (.02)

LA Area, CA .04 (.01) -.06 (.09) -.25 (.01) -.61 (.07)

Exhibit D2b. Point Estimates and Standard Errors for TFA Alum Analysis by Region, Year, and 

TFA Status

Subject

2017–18 2018–19

Reading Mathematics Reading Mathematics

Mathematics 

Overall

.09 (.13) .003 (.07) .11 (.06) .15 (.08)

NYC, NY .01 (.05) -.01 (.04) -.04 (.07) .00 (.07)

Camden, NJ .10 (.16) N/A .22 (.34) N/A

CPT, NC .02 (.02) .05 (.02) .01 (.01) .02 (.04)

ENC, NC -.02 (.01) .02 (.01) -.01 (.01) -.08 (.03)

Mountain West 

district

-.13 (.08) .02 (.12) .16 (.06) .08 (.04)

Clark County, NV .06 (.08) .13 (.06) .01 (.05) .11 (.08)

Bay Area, CA .42 (.08) -.07 (.01) .45 (.03) .54 (.02)

LA Area, CA .23 (.01) -.23 (.01) -.06 (.03) .01 (.01)
36 | AIR.ORG Teach For America National AmeriCorps Evaluation

Exhibit D3a. Student Descriptive Characteristics from Analytic Sample

(TFA Versus Non-TFA), Reading (N=40,757) 

Covariate TFA

(mean)

Non-TFA

(mean)

SMD SD

Prior Year Achievement -0.14 -0.14 0.00 0.97

Grade 4 0.11 0.14 -0.08 0.30

Grade 5 0.13 0.14 -0.02 0.32

Grade 6 0.21 0.24 -0.10 0.40

Grade 7 0.27 0.27 0.00 0.43

Grade 8 0.28 0.21 0.17 0.43

White 0.10 0.11 0.00 0.28

Black 0.30 0.27 0.06 0.40

Hispanic 0.49 0.52 -0.07 0.44

Asian 0.07 0.06 0.01 0.21

American Indian 0.00 0.00 -0.01 0.06

Native Hawaiian/Pacific Islander 0.00 0.00 0.00 0.05

Two or more races 0.02 0.02 0.01 0.14

Race Not specified 0.00 0.00 0.00 0.02

Female 0.49 0.49 0.01 0.50

EL 0.18 0.20 -0.05 0.35

Special education student 0.15 0.15 -0.01 0.35

Free or reduced-price lunch eligibility 0.81 0.81 -0.01 0.36

Student repeating current grade 0.01 0.01 0.01 0.07

Transient 0.13 0.12 0.00 0.25

Proportion of school year attended 0.94 0.94 -0.01 0.06

SMD = standardized mean difference; SD = standard deviation
37 | AIR.ORG Teach For America National AmeriCorps Evaluation

Exhibit D3b. Student Descriptive Characteristics from Analytic Sample

(TFA Versus Non-TFA), Mathematics (N=42,514) 

Covariate TFA

(mean)

Non-TFA

(mean)

SMD SD

Prior Year Achievement -0.23 -0.21 -0.03 0.92

Grade 4 0.13 0.13 -0.02 0.32

Grade 5 0.13 0.15 -0.06 0.32

Grade 6 0.22 0.27 -0.11 0.42

Grade 7 0.29 0.26 0.05 0.44

Grade 8 0.24 0.19 0.11 0.40

White 0.09 0.10 -0.07 0.27

Black 0.32 0.31 0.00 0.41

Hispanic 0.51 0.49 0.04 0.45

Asian 0.06 0.06 -0.02 0.21

American Indian 0.00 0.00 0.01 0.05

Native Hawaiian/Pacific Islander 0.00 0.00 0.00 0.05

Two or more races 0.02 0.02 -0.02 0.14

Race Not specified 0.00 0.00 0.00 0.02

Female 0.48 0.50 -0.03 0.50

EL 0.21 0.20 0.02 0.38

Special education student 0.14 0.15 -0.02 0.35

Free or reduced-price lunch eligibility 0.81 0.79 0.05 0.38

Student repeating current grade 0.01 0.00 0.01 0.07

Transient 0.10 0.09 0.01 0.25

Proportion of school year attended 0.94 0.94 -0.02 0.06

SMD = standardized mean difference; SD = standard deviation
38 | AIR.ORG Teach For America National AmeriCorps Evaluation

Exhibit D4a. Teacher Descriptive Characteristics from Analytic Sample

(TFA Versus Non-TFA), Reading (N=851) 

Covariate TFA

(mean)

Non-TFA

(mean)

SMD SD

Average Prior Year Student Achievement -0.33 -0.32 -0.02 0.55

Bachelor’s degree 0.89 0.85 0.12 0.25

Some postbaccalaureate 0.00 0.00 0.00 0.00

Master’s degree 0.10 0.14 -0.12 0.25

Doctorate 0.00 0.00 0.00 0.02

Students of color 0.92 0.90 0.10 0.13

Female students 0.48 0.46 0.10 0.12

Special education students 0.23 0.23 -0.08 0.24

EL students 0.23 0.26 -0.13 0.21

Students eligible for free or reduced-price lunch 0.84 0.82 0.13 0.17

Number of students 50 47 0 34

SMD = standardized mean difference; SD = standard deviation

Exhibit D4b. Teacher Descriptive Characteristics from Analytic Sample

(TFA Versus Non-TFA), Mathematics (N=788) 

Covariate TFA

(mean)

Non-TFA

(mean)

SMD SD

Average Prior Year Student Achievement -0.33 -0.36 0.04 0.53

Bachelor’s degree 0.91 0.84 0.19 0.26

Some postbaccalaureate 0.00 0.00 0.00 0.00

Master’s degree 0.09 0.16 -0.19 0.26

Doctorate 0.00 0.00 0.00 0.00

Students of color 0.92 0.90 0.18 0.12

Female students 0.48 0.48 0.01 0.10

Special education students 0.22 0.24 -0.11 0.24

EL students 0.24 0.23 0.04 0.17

Students eligible for free or reduced-price lunch 0.83 0.81 0.17 0.17

Number of students 56 52 0 39

SMD = standardized mean difference; SD = standard deviation
39 | AIR.ORG Teach For America National AmeriCorps Evaluation

Exhibit D5a. School Descriptive Demographics from Analytic Sample (TFA Versus Non-TFA), 

Reading (N=563) 

Covariate TFA

(mean)

Non-TFA

(mean)

SMD SD

Students of color 0.90 0.89 0.07 0.14

Female students 0.48 0.48 0.04 0.05

Special education students 0.16 0.17 -0.03 0.05

EL students 0.21 0.22 -0.02 0.12

Students eligible for free or reduced-price lunch 0.79 0.77 0.14 0.16

Number of students 640 650 0 289

SMD = standardized mean difference; SD = standard deviation

Exhibit D5b. School Descriptive Demographics from Analytic Sample (TFA Versus Non-TFA), 

Mathematics (N=499) 

Covariate TFA Non-TFA SMD SD

Students of color 0.89 0.89 0.06 0.13

Female students 0.48 0.48 -0.04 0.03

Special education students 0.16 0.16 0.04 0.04

EL students 0.21 0.22 -0.04 0.12

Students eligible for free or reduced-price lunch 0.76 0.75 0.15 0.18

Number of students 643 664 0 287

SMD = standardized mean difference; SD = standard deviation
40 | AIR.ORG Teach For America National AmeriCorps Evaluation

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