Students for Fair Admissions, Inc. v. President and Fellows of Harvard College et al

Filing 421

DECLARATION re 412 MOTION for Summary Judgment by Students for Fair Admissions, Inc.. (Attachments: # 1 Exhibit 1, # 2 Exhibit 2, # 3 Exhibit 3, # 4 Exhibit 4, # 5 Exhibit 5, # 6 Exhibit 6, # 7 Exhibit 7, # 8 Exhibit 8, # 9 Exhibit 9, # 10 Exhibit 10, # 11 Exhibit 11, # 12 Exhibit 12, # 13 Exhibit 13, # 14 Exhibit 14, # 15 Exhibit 15, # 16 Exhibit 16, # 17 Exhibit 17, # 18 Exhibit 18, # 19 Exhibit 19, # 20 Exhibit 20, # 21 Exhibit 21, # 22 Exhibit 22, # 23 Exhibit 23, # 24 Exhibit 24, # 25 Exhibit 25, # 26 Exhibit 26, # 27 Exhibit 27, # 28 Exhibit 28, # 29 Exhibit 29, # 30 Exhibit 30, # 31 Exhibit 31, # 32 Exhibit 32, # 33 Exhibit 33, # 34 Exhibit 34, # 35 Exhibit 35, # 36 Exhibit 36, # 37 Exhibit 37, # 38 Exhibit 38, # 39 Exhibit 39, # 40 Exhibit 40, # 41 Exhibit 41, # 42 Exhibit 42, # 43 Exhibit 43, # 44 Exhibit 44, # 45 Exhibit 45, # 46 Exhibit 46, # 47 Exhibit 47, # 48 Exhibit 48, # 49 Exhibit 49, # 50 Exhibit 50, # 51 Exhibit 51, # 52 Exhibit 52, # 53 Exhibit 53, # 54 Exhibit 54, # 55 Exhibit 55, # 56 Exhibit 56, # 57 Exhibit 57, # 58 Exhibit 58, # 59 Exhibit 59, # 60 Exhibit 60, # 61 Exhibit 61, # 62 Exhibit 62, # 63 Exhibit 63, # 64 Exhibit 64, # 65 Exhibit 65, # 66 Exhibit 66, # 67 Exhibit 67, # 68 Exhibit 68, # 69 Exhibit 69, # 70 Exhibit 70, # 71 Exhibit 71, # 72 Exhibit 72, # 73 Exhibit 73, # 74 Exhibit 74, # 75 Exhibit 75, # 76 Exhibit 76, # 77 Exhibit 77, # 78 Exhibit 78, # 79 Exhibit 79, # 80 Exhibit 80, # 81 Exhibit 81, # 82 Exhibit 82, # 83 Exhibit 83, # 84 Exhibit 84, # 85 Exhibit 85, # 86 Exhibit 86, # 87 Exhibit 87, # 88 Exhibit 88, # 89 Exhibit 89, # 90 Exhibit 90, # 91 Exhibit 91, # 92 Exhibit 92, # 93 Exhibit 93, # 94 Exhibit 94, # 95 Exhibit 95, # 96 Exhibit 96, # 97 Exhibit 97, # 98 Exhibit 98, # 99 Exhibit 99, # 100 Exhibit 100, # 101 Exhibit 101, # 102 Exhibit 102, # 103 Exhibit 103, # 104 Exhibit 104, # 105 Exhibit 105, # 106 Exhibit 106, # 107 Exhibit 107, # 108 Exhibit 108, # 109 Exhibit 109, # 110 Exhibit 110, # 111 Exhibit 111, # 112 Exhibit 112, # 113 Exhibit 113, # 114 Exhibit 114, # 115 Exhibit 115, # 116 Exhibit 116, # 117 Exhibit 117, # 118 Exhibit 118, # 119 Exhibit 119, # 120 Exhibit 120, # 121 Exhibit 121, # 122 Exhibit 122, # 123 Exhibit 123, # 124 Exhibit 124, # 125 Exhibit 125, # 126 Exhibit 126, # 127 Exhibit 127, # 128 Exhibit 128, # 129 Exhibit 129, # 130 Exhibit 130, # 131 Exhibit 131, # 132 Exhibit 132, # 133 Exhibit 133, # 134 Exhibit 134, # 135 Exhibit 135, # 136 Exhibit 136, # 137 Exhibit 137, # 138 Exhibit 138, # 139 Exhibit 139, # 140 Exhibit 140, # 141 Exhibit 141, # 142 Exhibit 142, # 143 Exhibit 143, # 144 Exhibit 144, # 145 Exhibit 145, # 146 Exhibit 146, # 147 Exhibit 147, # 148 Exhibit 148, # 149 Exhibit 149, # 150 Exhibit 150, # 151 Exhibit 151, # 152 Exhibit 152, # 153 Exhibit 153, # 154 Exhibit 154, # 155 Exhibit 155, # 156 Exhibit 156, # 157 Exhibit 157, # 158 Exhibit 158, # 159 Exhibit 159, # 160 Exhibit 160, # 161 Exhibit 161, # 162 Exhibit 162, # 163 Exhibit 163, # 164 Exhibit 164, # 165 Exhibit 165, # 166 Exhibit 166, # 167 Exhibit 167, # 168 Exhibit 168, # 169 Exhibit 169, # 170 Exhibit 170, # 171 Exhibit 171, # 172 Exhibit 172, # 173 Exhibit 173, # 174 Exhibit 174, # 175 Exhibit 175, # 176 Exhibit 176, # 177 Exhibit 177, # 178 Exhibit 178, # 179 Exhibit 179, # 180 Exhibit 180, # 181 Exhibit 181, # 182 Exhibit 182, # 183 Exhibit 183, # 184 Exhibit 184, # 185 Exhibit 185, # 186 Exhibit 186, # 187 Exhibit 187, # 188 Exhibit 188, # 189 Exhibit 189, # 190 Exhibit 190, # 191 Exhibit 191, # 192 Exhibit 192, # 193 Exhibit 193, # 194 Exhibit 194, # 195 Exhibit 195, # 196 Exhibit 196, # 197 Exhibit 197, # 198 Exhibit 198, # 199 Exhibit 199, # 200 Exhibit 200, # 201 Exhibit 201, # 202 Exhibit 202, # 203 Exhibit 203, # 204 Exhibit 204, # 205 Exhibit 205, # 206 Exhibit 206, # 207 Exhibit 207, # 208 Exhibit 208, # 209 Exhibit 209, # 210 Exhibit 210, # 211 Exhibit 211, # 212 Exhibit 212, # 213 Exhibit 213, # 214 Exhibit 214, # 215 Exhibit 215, # 216 Exhibit 216, # 217 Exhibit 217, # 218 Exhibit 218, # 219 Exhibit 219, # 220 Exhibit 220, # 221 Exhibit 221, # 222 Exhibit 222, # 223 Exhibit 223, # 224 Exhibit 224, # 225 Exhibit 225, # 226 Exhibit 226, # 227 Exhibit 227, # 228 Exhibit 228, # 229 Exhibit 229, # 230 Exhibit 230, # 231 Exhibit 231, # 232 Exhibit 232, # 233 Exhibit 233, # 234 Exhibit 234, # 235 Exhibit 235, # 236 Exhibit 236, # 237 Exhibit 237, # 238 Exhibit 238, # 239 Exhibit 239, # 240 Exhibit 240, # 241 Exhibit 241, # 242 Exhibit 242, # 243 Exhibit 243, # 244 Exhibit 244, # 245 Exhibit 245, # 246 Exhibit 246, # 247 Exhibit 247, # 248 Exhibit 248, # 249 Exhibit 249, # 250 Exhibit 250, # 251 Exhibit 251, # 252 Exhibit 252, # 253 Exhibit 253, # 254 Exhibit 254, # 255 Exhibit 255, # 256 Exhibit 256, # 257 Exhibit 257, # 258 Exhibit 258, # 259 Exhibit 259, # 260 Exhibit 260, # 261 Exhibit 261)(Consovoy, William) (Additional attachment(s) added on 6/18/2018: # 262 Unredacted version of Declaration, # 263 Exhibit 1 (filed under seal), # 264 Exhibit 2 (filed under seal), # 265 Exhibit 5 (filed under seal), # 266 Exhibit 6 (filed under seal), # 267 Exhibit 7 (filed under seal), # 268 Exhibit 8 (filed under seal), # 269 Exhibit 9 (filed under seal), # 270 Exhibit 10 (filed under seal)) (Montes, Mariliz). (Additional attachment(s) added on 6/18/2018: # 271 Exhibit 11 (filed under seal), # 272 Exhibit 12(filed under seal), # 273 Exhibit 13 (filed under seal), # 274 Exhibit 14 (filed under seal), # 275 Exhibit 16 (filed under seal), # 276 Exhibit 17(filed under seal), # 277 Exhibit 18(filed under seal), # 278 Exhibit 19 (filed under seal), # 279 Exhibit 20 (filed under seal), # 280 Exhibit 22 (filed under seal), # 281 Exhibit 23 (filed under seal), # 282 Exhibit 24 (filed under seal), # 283 Exhibit 25(filed under seal), # 284 Exhibit 26 (filed under seal), # 285 Exhibit 28 (filed under seal), # 286 Exhibit 29 (filed under seal), # 287 Exhibit 31 (filed under seal), # 288 Exhibit 32 (filed under seal), # 289 Exhibit 33 (filed under seal), # 290 Exhibit 35 (filed under seal), # 291 Exhibit 36 (filed under seal), # 292 Exhibit 37 (filed under seal), # 293 Exhibit 38(filed under seal), # 294 Exhibit 39 (filed under seal), # 295 Exhibit 40 (filed under seal), # 296 Exhibit 41, # 297 Exhibit 42 (filed under seal), # 298 Exhibit 43 (filed under seal), # 299 Exhibit 44(filed under seal), # 300 Exhibit 45 (filed under seal), # 301 Exhibit 46 (filed under seal), # 302 Exhibit 47 (filed under seal), # 303 Exhibit 48 (filed under seal), # 304 Exhibit 51 (filed under seal)) (Montes, Mariliz).

UNITED STATES DISTRICT COURT
FOR THE DISTRICT OF MASSACHUSETTS

STUDENTS FOR FAIR ADMISSIONS,
INC.,

Civil Action No. 1:14-cv-14176

Plaintiff,
v.
PRESIDENT AND FELLOWS OF
HARVARD COLLEGE (HARVARD
CORPORATION),
Defendant.

REPORT OF DAVID CARD, Ph.D.
December 15, 2017

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Table of Contents
1. QUALIFICATIONS.................................................................................................................................................. 3 
2. ASSIGNMENT AND SUMMARY OF OPINIONS ............................................................................................ 5 
2.1. Assignment ....................................................................................................................................5 
2.2. Overview of report and summary of findings...............................................................................6 
3. AN OVERVIEW OF HARVARD’S APPLICANT POOL AND ADMISSIONS PROCESS....................12 
3.1. Harvard’s admissions process is highly competitive, and academic achievement is
abundant in its applicant pool ................................................................................................12 
3.2. Harvard seeks candidates with a wide range of skills beyond academic achievement .............16 
3.3. Harvard’s decision process is labor-intensive and seeks to understand the full context
of each applicant’s high school achievements .......................................................................23 
3.4. Harvard’s ratings reflect important and otherwise unobservable information about the
academic and non-academic qualifications of applicants .....................................................25 
3.5. Prof. Arcidiacono’s statistical model fails to account for numerous dimensions of
Harvard’s admissions process ................................................................................................31 
4. ACCOUNTING FOR NON-ACADEMIC AND CONTEXTUAL FACTORS IS CRITICAL IN
MODELING HARVARD’S ADMISSIONS PROCESS ..............................................................................33 
4.1. There is no statistically significant difference in admission rates for the vast majority
of Asian-American and White applicants ..............................................................................34 
4.2. White applicants have relatively stronger qualifications on non-academic dimensions ..........35 
4.3. Prof. Arcidiacono’s model excludes available measures of life circumstance and
context .....................................................................................................................................40 
5. A MORE COMPLETE STATISTICAL MODEL SHOWS NO EVIDENCE OF BIAS AGAINST
ASIAN-AMERICAN APPLICANTS ..............................................................................................................46 
5.1. Important differences between Prof. Arcidiacono’s methodology and mine ............................46 
5.2. My enriched model finds no statistically significant evidence of bias.......................................62 
5.3. Analysis of key subgroups of the data further contradicts SFFA’s claim of systematic
bias...........................................................................................................................................75 
5.4. Conclusion ..................................................................................................................................79 
6. AVAILABLE DATA DO NOT INDICATE THAT RACE IS A DETERMINATIVE FACTOR IN
ADMISSIONS AT HARVARD..........................................................................................................................81 
6.1. Race is less important than other factors in admissions decisions............................................82 
6.2. Race is less important than unmeasured, individualized factors ..............................................85 
6.3. Prof. Arcidiacono’s claim about a “floor” for the admission rate of African-American
applicants is not supported by available data .........................................................................87 
6.4. Conclusion ..................................................................................................................................93 
7. ANALYSIS OF POTENTIAL RACE-NEUTRAL ALTERNATIVES ..........................................................95 

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 1

7.1. Race-neutral alternatives identified in academic literature and by SFFA ...............................95 
7.2. Academic research indicates that race-neutral alternatives diminish universities’
ability to select for quality .......................................................................................................97 
7.3. Analysis of race-neutral alternatives using Harvard’s admissions data .................................103 
7.4. Mr. Kahlenberg’s simulated race-neutral practices, like others considered above,
could achieve a comparably diverse class only by changing the class in significant
ways and compromising its quality .......................................................................................151 
7.5. Conclusion ................................................................................................................................153 
8. APPENDIX A..........................................................................................................................................................155 
9. APPENDIX B..........................................................................................................................................................171 
9.1. Documents relied upon .............................................................................................................171 
10. APPENDIX C .......................................................................................................................................................178 
10.1. Parent occupations..................................................................................................................178 
11. APPENDIX D .......................................................................................................................................................180 
11.1. Primary activities ....................................................................................................................180 
12. APPENDIX E .......................................................................................................................................................181 
12.1. Variables used in logit model of admissions ..........................................................................181 
13. APPENDIX F........................................................................................................................................................187 
13.1. Mr. Kahlenberg’s Simulations ...............................................................................................187 

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 2

1. QUALIFICATIONS
1. I received a B.A. degree in Economics from Queen’s University (in Canada) in 1978 and a
Ph.D. in Economics from Princeton University in 1983. From 1982 to 1983, I was an Assistant
Professor at the University of Chicago Graduate School of Business. From 1983 to 1997, I held
positions as Assistant Professor and Professor of Economics at Princeton University. Since 1997, I
have been the Class of 1950 Professor of Economics at the University of California, Berkeley.
2. I have published more than 110 articles and book chapters, co-authored one book, and coedited seven others, including the Handbook of Labor Economics. The majority of my publications
are focused on labor economics—the field of economics that addresses questions related to
discrimination in various contexts, including education. My articles have appeared in the leading
journals in economics and econometrics, including Econometrica, the American Economic Review,
the Quarterly Journal of Economics, the Journal of Political Economy, and the Journal of
Econometrics. I served as co-editor of the American Economic Review from 2002 to 2005 and coeditor of Econometrica from 1993 to 1997. I have also served on several editorial boards and
government advisory committees for statistical issues, including the National Academy of Science
Committee on National Statistics (2012 – 2015), the U.S. Census Advisory Committee (1991 –
1996), Statistics Canada’s Labour Statistics Advisory Committee (1990 – 2002), and the National
Institutes of Health Social Sciences, Nursing, Epidemiology, and Methods Review Panel (1998 –
2003).
3. My research has been recognized by several awards and prizes, including election as a
Fellow of the American Academy of Arts and Sciences in 1998, a Fellow of the Econometric Society
in 1992, and a Fellow of the Society of Labor Economics in 2004. In 1995, I received the John Bates
Clark Medal, widely regarded as one of the highest honors in the field of economics, which is
awarded by the American Economic Association to the outstanding economist in the United States
under the age of 40. In 2006, I was awarded the IZA Prize by the Institute for the Study of Labor in
Bonn for outstanding academic achievement in the field of labor economics. In 2008, I was awarded
the Frisch Medal by the Econometric Society for the best article in applied economics published in
Econometrica in the previous two years. I was the co-recipient of the 2015 BBVA Foundation
Frontiers of Knowledge Award in economics.
4. My research focuses on statistical analysis of the labor market and related data pertaining to
such issues as wages, hours of work, employment, education, and immigration. I have published
multiple studies analyzing differential labor-market outcomes across race and gender (including
questions of discrimination), as well as a study of the effects of race-conscious admissions. In my
capacity as a journal editor, member of an editorial board, and member of proposal review

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 3

committees, I have also edited, refereed, and critiqued many studies that address questions of
discrimination, education, and/or college admissions. My complete CV, which includes a list of
publications I have authored within the past ten years, is attached in Appendix A.
5. I am being compensated at my standard billing rate of $750 per hour. I have been assisted
in this matter by staff of Cornerstone Research, who worked under my direction. In addition to
compensation at my hourly rate, I receive compensation from Cornerstone Research based on its
collected billings for supporting me in this matter. None of my compensation in this matter is in any
way contingent or based on the content of my opinion in this or any other matter or the outcome of
this or any other matter. A list of my testimony in the last four years is attached in Appendix A.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 4

2. ASSIGNMENT AND SUMMARY OF OPINIONS
2.1. Assignment
6. Harvard’s counsel have asked me to assess the following questions related to Harvard’s
admissions process, which I understand are relevant to the claims of the Plaintiff, Students for Fair
Admissions, Inc. (“SFFA”), in this matter on the basis of the complaint and SFFA’s expert reports:
• Does statistical evidence support SFFA’s claim that Harvard
discriminates against Asian-American applicants in undergraduate
admissions decisions?
• Does statistical evidence support SFFA’s claim that race is the
determinative factor in undergraduate admissions decisions for many
applicants?
• Is there statistical evidence that Harvard has engaged in racial balancing
in its undergraduate admissions process?
• How would the racial composition and other attributes of Harvard’s
admitted class be expected to change if Harvard stopped considering
race and instead pursued a variety of race-neutral ways of seeking to
increase the racial diversity of its admitted class?
• Are the analyses and conclusions offered by SFFA’s experts reliable?
7. In attempting to answer these questions, I have relied on several sources of information,
including deposition testimony in this matter, documents produced by Harvard in this matter,
database information produced by Harvard in this matter (covering all applicants to the classes of
2014 to 2019),1 College Board data on neighborhood and high school demographics and high school
quality produced in this matter, relevant public information and data, and academic research. I have
also reviewed the reports submitted by SFFA from Professor Peter Arcidiacono and Mr. Richard

1

Prof. Arcidiacono states that the list of data Harvard produced and omitted can be found at HARV00006413,
HARV00006471, HARV00006541, HARV00006607, HARV00006695, and HARV00006759. A list of additional
database fields produced by Harvard is available at HARV00001322 – HARV00001361.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 5

Kahlenberg and their relevant supporting materials.2
8. Appendix B to this report lists the documents on which I relied in forming the opinions
expressed in this report.
2.2. Overview of report and summary of findings
9. SFFA’s Complaint3 and expert reports claim that Harvard’s undergraduate admissions
decisions exhibit bias against Asian-American applicants, that race is a determinative factor in the
Harvard admissions process for many applicants, and that Harvard can achieve its diversity goals
without considering race by using a variety of race-neutral admissions practices.
10. SFFA’s claim of discrimination against Asian-American applicants relies most
fundamentally on the premise that Asian-American applicants are admitted at a lower rate than White
applicants, while possessing higher academic credentials than White applicants on average. As I
explain in this report, however, there is a critical flaw in SFFA’s reasoning: as I understand from my
review of the documents and testimony in this matter, and as my empirical analysis corroborates,
Harvard’s admissions process values many dimensions of excellence, not just prior academic
achievement.
11. As I detail in Section 3 below, Harvard’s applicant pool is full of students with
outstanding academic credentials. More than 8,000 applicants for the class of 2019 had perfect GPAs,
approximately 3,500 applicants had perfect SAT math scores, and nearly 1,000 applicants had perfect
ACT and/or SAT composite scores. In that pool, having strong academic credentials is not sufficient
to make an applicant a strong candidate for admission. The record in this case makes clear that it is
often the non-academic aspects of a candidate’s application that determine whether the candidate is
admitted from this academically exceptional pool, that the evaluation of each candidate takes into
account the full context of his or her life experiences, and that Harvard’s ultimate goal is to admit a
student body that exhibits excellence in a variety of forms and includes students with diverse
experiences, backgrounds, skills, and interests. Harvard’s admissions data are consistent with these
facts. They show, for example, that candidates who are strong on dimensions other than academics
are rarer than academically strong candidates. They also show that candidates who receive high
ratings in at least three of the four categories rated by admissions officers (academic, extracurricular,
athletic, and personal)—referred to in this report as candidates who are “multi-dimensional”—have a
2

Expert Report of Peter S. Arcidiacono, Students for Fair Admissions, Inc. v. President and Fellows of Harvard College,
October 16, 2017 (“Arcidiacono Report”); Expert Report of Richard D. Kahlenberg, Students for Fair Admissions, Inc. v.
President and Fellows of Harvard College, October 16, 2017 (“Kahlenberg Report”).
3
Complaint, Students for Fair Admissions, Inc. v. President and Fellows of Harvard College (Harvard Corporation); and
the Honorable and Reverend the Board of Overseers, November 17, 2014 (“Complaint”).
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 6

high admission rate and compose a much larger share of the admitted class than candidates who are
exceptional on just one dimension.
12. Prof. Arcidiacono reveals a significant misunderstanding of Harvard’s admissions process
by focusing so much of his analysis on academic achievement. For example, four of the six
regression models that Prof. Arcidiacono offers do not include controls for the three non-academic
ratings (extracurricular, personal, and athletic), which are central to Harvard’s evaluation of
candidates for admission. And Prof. Arcidiacono accounts in only a crude and limited way for
considerations of high school quality and socioeconomic background that Harvard uses to place in
context each applicant’s prior academic achievement. Such analyses are fundamentally flawed and
unreliable because they fail to account for the multi-dimensional evaluation Harvard employs when
rendering its admissions decisions.
13. As I explain in Section 4, Prof. Arcidiacono attempts to justify his focus on academics by
presenting a variety of basic descriptive analyses that purport to show a broad correlation between
Harvard’s academic index and non-academic qualifications that Harvard considers. He then argues
that it is reasonable to assume that Asian-American applicants are stronger than applicants of other
races in non-academic respects (including factors he cannot measure and include in his model)
because they are stronger on academic measures. That is a central assumption of his analysis—and,
as I demonstrate in Section 4, it is wrong. A more careful examination of the data shows that White
applicants are stronger than Asian-American applicants, in aggregate, across the three non-academic
dimensions that Harvard rates (athletic, extracurricular, and personal), and that they are more likely to
exhibit multi-dimensional excellence (i.e., receive high ratings in at least three of the four categories).
In fact, Prof. Arcidiacono’s own analysis shows that, across all of the non-academic variables he
includes in his regression model, White applicants in aggregate are stronger than Asian-American
applicants. Because non-academic factors are much harder to quantify than academic factors, and
thus fewer of them are observable in the Harvard admissions database, there is a strong possibility
that statistical models like those developed by Prof. Arcidiacono will exclude important nonacademic factors, and will therefore be biased in favor of finding a race-based disparity in admissions
between Asian-American and White applicants. That is, it is quite possible that if one could control
more extensively for non-academic factors, those factors—and not race—would explain any disparity
in the admission rate between Asian-American and White applicants.
14. In Section 4, I also explain how Prof. Arcidiacono’s models include very little
information that can account for the overall context of each candidate’s application, such as the
quality of the applicant’s high school, the applicant’s socioeconomic circumstances, and the
resources and opportunities available to the applicant as a result of his high school, neighborhood,
and family background. This contextual information is critical in the admissions process, because

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 7

Harvard recognizes that one cannot evaluate a student’s grades, standardized test scores, or other
attributes without understanding the circumstances in which the applicant grew up. For that reason,
the admissions process is designed to ensure that admissions officers have detailed knowledge of
many of the high schools and neighborhoods from which applicants apply, and that admissions
officers examine each applicant’s file in light of that context. Importantly, as I show below in Section
4, Prof. Arcidiacono failed to make use of a variety of such contextual factors that were available in
data produced to him, and that differ on average between Asian-American and White applicants.
15. In Section 5, I turn to a more formal statistical analysis of the difference in admission
rates between White and Asian-American applicants. This analysis shows that the purported “penalty
against Asian Americans” identified by Prof. Arcidiacono does not actually exist.4 Prof.
Arcidiacono’s finding is instead driven by two limitations of his model.
16. First, as noted above, his model does not account for numerous critical factors in the
available data that provide important context for each application, including measures of applicants’
socioeconomic status (such as the demographics of their neighborhoods), the quality of their high
schools, and other variables that can reflect differences in life experiences and opportunities. Prof.
Arcidiacono’s own models show that the factors of this type that he does include in his model help
explain the disparity in admission rates between White and Asian-American applicants, which is why
it is problematic that Prof. Arcidiacono does not control for more of them. Once Prof. Arcidiacono’s
model is modified to account for these additional factors, it finds no evidence of a racial disparity in
admissions decisions.
17. Second, Prof. Arcidiacono’s model combines data from multiple admissions cycles, thus
imposing the assumption that Harvard compares applicants across years rather than simply within
each year’s application pool. As I detail below, that assumption is unreasonable. Each admissions
cycle is different, and the data confirm as much, showing that the estimated effect of various factors
on an applicant’s probability of admission changes substantially from year to year. Importantly, when
I analyze the data year-by-year, as the evidence supports, I find that the model’s predictive accuracy
increases. My year-by-year analysis finds that the estimated effect of Asian-American ethnicity on
applicants’ probability of admission is not statistically significant in any year, or even on average
across all six years, and is actually positive in four of six years.
18. It is important to note that even when I enrich the model to account for additional control
variables and to account for differences in the admissions process from year to year, the model still
does not perfectly capture all of the information on which the Harvard Admissions Committee relies
when making admissions decisions. This problem is what I refer to throughout the report as a
4

Arcidiacono Report, p. 61.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 8

“missing data” problem—a problem that exists when modeling any complex decision process (like
admissions to Harvard) in which decisionmakers consider many factors that are hard to quantify. The
data I am discussing are “missing” because they are not quantified in Harvard’s database or because
they are inherently difficult to quantify. Importantly, because non-academic factors that are a relative
strength of White applicants (on average) are harder to quantify than academic factors, it is likely that
additional such factors remain missing from the model even after I enrich the model to capture more
information on non-academic factors.
19. In Section 5, I also address Prof. Arcidiacono’s claim that Harvard’s personal and overall
ratings are biased against Asian-American applicants. In the case of the personal rating, the statistical
evidence Prof. Arcidiacono offers in support of this claim is weak for two key reasons. First, the
ordered logit models that Prof. Arcidiacono uses to try to isolate the effect of race on the personal
rating are, by his own measure of statistical reliability, weak—that is, the models explain only a
relatively small fraction of the differences across candidates in the personal ratings. A key reason for
this is that the available admissions data include only a few quantitative variables that can be used to
model variation in the personal rating. In essence, the “missing data” problem I describe above is
particularly severe for the assessment of personal ratings, which depend largely on qualitative factors
that cannot be captured in Harvard’s database. For example, testimony in the record indicates that the
applicant’s essay is an important consideration in the personal rating, but there is no quantifiable
measure of the essay in the data I analyze. This means that the disparity Prof. Arcidiacono labels
“bias” may very well be explained by factors other than race that the model does not include.
Importantly, Prof. Arcidiacono’s own model finds that the estimated negative effect of AsianAmerican ethnicity on the personal rating shrinks as non-academic factors are added to the model.
This pattern suggests that the estimated effect would shrink further if one could quantify the missing
data that the Harvard admissions officers use to form their assessments.
20. Another reason to be skeptical of the reliability of Prof. Arcidiacono’s model of the
personal rating is that his model of the academic rating—which is the most reliable of any of his
ratings models—shows that Asian-American ethnicity has an estimated positive and significant effect
on that rating. So does his model of the extracurricular rating. Given these results, one of two things
must be true. Either (1) Harvard is engaging in an exceptionally unusual form of discrimination, in
which it is favoring Asian-American applicants in the academic and extracurricular ratings only to
penalize them in the personal and overall ratings, or (2) Prof. Arcidiacono’s ratings models are
simply not reliable enough to measure all of the differences between Asian-American and White
applicants on the various dimensions valued by Harvard that drive the assignment of ratings.
21. While Prof. Arcidiacono provides no reliable evidence that the personal rating is biased
against Asian-American applicants—and while excluding that rating from a model of admissions is

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 9

problematic because the rating plays a significant role in the admissions process and incorporates
data on the qualities of the applicants that are otherwise missing—I agree with Prof. Arcidiacono that
the overall rating should be excluded from the model. Testimony in this case indicates that an
applicant’s race may have a direct effect on her overall rating, and it is a well-accepted statistical
practice to exclude variables from a regression model that may themselves be directly influenced by
the variable of interest (here, race). While I have excluded the overall rating from my admissions
model, I also believe that the model of overall ratings developed by Professor Arcidiacono is too
weak to provide reliable statistical evidence of “bias” in the assignment of this rating. Like Prof.
Arcidiacono’s models of the ratings in general, the overall-rating model leaves unexplained a large
proportion of the variation in the overall ratings and cannot control for numerous factors that may
influence the overall rating and may be correlated with race.
22. Despite my view that removing the personal rating from the model is a flawed approach, I
also implement an analysis that assumes for the sake of argument that the personal rating may be
biased and removes it (as well as the overall rating) from the model altogether. This is an extremely
conservative approach, because it removes the personal rating from the model entirely—not just the
supposedly biased component of the rating—even though Prof. Arcidiacono’s own analysis shows
that, when the supposed bias is statistically eliminated from the personal rating, White applicants’
personal ratings are still on average slightly higher than those of Asian-American applicants.
Nevertheless, using this very conservative model, I still find no evidence of a statistically significant
negative association between Asian-American ethnicity and applicants’ likelihood of admission in
five of the six admissions cycles for which data are available.
23. In Section 6, I assess how the race of African-American, Hispanic, and Other (non-Asian)
minority race (AHO) candidates affects their likelihood of admission, in order to respond to Prof.
Arcidiacono’s argument that race has a large effect for such candidates.5 I reach several conclusions
on this issue. First, consistent with testimony from Harvard witnesses, I find that although AHO
ethnicity is associated with a significantly higher likelihood of admission, the importance of race in
explaining admission decisions is much smaller than that of many other factors Harvard considers.
Second, I show that race plays only a small role in admissions outcomes for the vast majority of
applicants. And for the small number of applicants for whom race plays a more significant role, other
non-race factors also substantially affect the applicants’ likelihood of admission. Third, I find that the
estimated effect of race for almost all AHO applicants is smaller than that of individualized
“unobservable” factors that cannot be quantified by a statistical model. Taken together, these results
suggest that, while race plays a role in admission decisions—by design—it is just one of many factors

5

Other minority race applicants include applicants classified as Native American or Hawaiian/Pacific Islander under
Harvard’s “old methodology,” the race definition that Prof. Arcidiacono uses throughout his report (Arcidiacono Report,
p. 23).
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 10

Harvard considers in its whole-person review of each candidate. I also examine Prof. Arcidiacono’s
claim that Harvard has recently imposed a floor on the admission rate of African-American
applicants and find no evidence to support that claim.
24. In Section 7, I turn to a final question: are there race-neutral admissions practices that
Harvard could implement that would allow it to achieve its diversity objectives, without lowering the
quality of its class on other dimensions that it values? Using the admissions model developed in
Section 5, I simulate how various race-neutral admissions practices (both alone and in combination)
would affect the demographic and other characteristics of the admitted class. I show that Harvard
could achieve comparable ethnic and racial diversity by other means, but that doing so would
produce a student body that is less exceptional on multiple dimensions that I understand Harvard
values (such as academic credentials, extracurricular achievement, and personal qualities).
25. In performing my analysis in Section 7, I also assess the literature analyzed and
simulations offered by Mr. Kahlenberg. I generally agree with Mr. Kahlenberg that race-neutral
alternatives can sometimes be used to help universities increase racial diversity. As I explain below,
however, the relevant question here is not whether some universities could achieve diversity without
considering race but whether Harvard could do so, and furthermore whether doing so would harm
Harvard’s other institutional and educational objectives. A direct analysis of Harvard’s data is needed
to answer that question. With regard to Mr. Kahlenberg’s simulations of race-neutral alternatives, I
show using Mr. Kahlenberg’s own data that the proposed alternatives he considers either lead to a
significantly less diverse class, or to a class that is comparably diverse but far weaker in other
dimensions that I understand Harvard values, such as academic quality.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 11

3. AN OVERVIEW OF HARVARD’S APPLICANT POOL AND ADMISSIONS PROCESS
26. The first step in my analysis is a careful review of the discovery in this case concerning
Harvard’s admissions process. The purpose of this review is to understand what factors Harvard
values when admitting students. As noted above, SFFA’s claim of bias against Asian-American
applicants relies centrally on the premise that Asian-American applicants have the strongest academic
qualifications on average across racial groups, but are admitted at a lower rate than applicants of
other races. SFFA’s expert, Prof. Arcidiacono, focuses much of his analysis on academic
qualifications. It is essential, however, to understand what other factors Harvard considers when
evaluating candidates, and how important those factors are relative to academic credentials in
explaining the variability in admissions outcomes.
27. In the remainder of this section, I summarize the key features of Harvard’s
decisionmaking process. I start with an analysis of the size of Harvard’s applicant pool and the
competitive nature of admissions decisions. I show that superb standardized test scores and GPAs are
abundant among Harvard applicants, with thousands of candidates having perfect GPAs and/or SAT
and ACT scores. It is impossible for Harvard to admit all applicants with exceptional academic
credentials, and so focusing too much on such credentials when trying to understand admissions
decisions (as Prof. Arcidiacono does) is the wrong approach.
28. I then summarize relevant information in the record that identifies the broader set of
characteristics that Harvard seeks in the students it admits. Documents and testimony show that
Harvard values candidates who can contribute to both academic and non-academic dimensions of
campus life, and that Harvard considers the full context of an applicant’s life experience (including
the quality of her high school, the characteristics of her home neighborhood, and her family
background) when deciding whom to admit. Those facts will be critical to the statistical analyses I
offer in Sections 4 and 5. An important difference between my analysis and Prof. Arcidiacono’s is
that my analysis includes a much richer set of control variables, including more detailed controls for
applicants’ socioeconomic status (as measured by the demographic characteristics of their
neighborhoods and high schools as well as their parents’ occupations) that more accurately reflect
and account for the many different factors Harvard weighs in its whole-person admissions process.
3.1. Harvard’s admissions process is highly competitive, and academic achievement is abundant in its
applicant pool
29. Harvard’s admissions process is one of the most competitive and selective in the country.
For example, more than 37,000 high school students applied to Harvard for admission to the class of

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 12

2019, but only 2,003 were admitted, leading to an admission rate of 5.37%.6 According to U.S. News
and World Report, Harvard had the third-lowest admission rate among U.S. universities in Fall
2016.7
30. Exhibit 1 shows the number of domestic applicants, number of domestic admitted
students, and the admission rate to Harvard for domestic applicants each year for the classes of 2014
to 2019 (the years for which admissions data were produced in this matter).8 As the table shows,
Harvard’s domestic applicant pool has grown since the class of 2014 admissions cycle, while the
number of admitted domestic students has fallen, making Harvard’s admissions process for domestic
students even more competitive in recent years. More domestic candidates now apply each year for
fewer spots, and as a result Harvard’s admission rate has declined consistently over time from 8.75%
to 6.61%.

Domestic applicants, admitted students, and admission rates at Harvard by year

Source: Arcidiacono Data
Note: Data are from applicants to the classes of 2014 – 2019 using Professor Arcidiacono’s expanded sample.

6

The admission rate of 5.37% includes all applicants and admitted students, including international students. Analyses in
the remainder of this report are limited to domestic applicants, consistent with Prof. Arcidiacono’s definition (see
workpaper).
7
U.S. News and World Report, “Top 100 Lowest Acceptance Rates,” available at https://www.usnews.com/bestcolleges/rankings/lowest-acceptance-rate, accessed December 7, 2017.
8
I follow Prof. Arcidiacono by defining “domestic” applicants as those who are U.S. citizens or permanent residents, and
in limiting my analyses to domestic applicants. Throughout my analyses, I primarily rely on Prof. Arcidiacono’s
produced, processed dataset (the “Arcidiacono Data”), which is constructed using the data produced by Harvard in this
litigation. I also use a version of Prof. Arcidiacono’s produced dataset that is augmented with additional variables from
the College Board and Harvard’s underlying data and that reflects a few technical corrections, which I refer to as the
“Augmented Arcidiacono Data.”
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 13

31. In addition to having a relatively small number of places available in its freshman class
for a large number of applicants, Harvard also has an applicant pool with extraordinary academic
qualifications. As shown in Exhibit 2, nearly 3,500 domestic applicants to the class of 2019 had
perfect math SAT scores. Additionally, more than 8,000 domestic applicants had a perfect converted
GPA (based on Harvard’s GPA index, which normalizes GPAs across high schools), 625 earned
perfect composite ACT scores, 361 earned a perfect 2400 on the SAT, and thousands had average
SAT subject test scores of 700 or higher. As shown in Exhibit 3, domestic students admitted to
Harvard’s class of 2019 had mean and median SAT scores of 2241 and 2270, respectively, and mean
and median ACT scores of 33 and 34, as well as an average converted GPA of 77 out of 80.
32. These data show that even if Harvard wanted to admit every student with elite academic
credentials, it could not. Harvard admits roughly 1,800 domestic students each year, yet thousands of
applicants have impeccable academic qualifications.9 For example, based on the statistics in Exhibit
2, even if Harvard sought to admit only applicants with a perfect GPA, it would need to reject at least
6,000 such applicants and all other domestic applicants. Similarly, even if Harvard sought to admit
only applicants with a perfect Math SAT score, it would need to reject nearly 2,000 such applicants
and all other domestic applicants.

9

See workpaper.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 14

Many applicants to the class of 2019 had outstanding standardized test scores and grades

Source: Arcidiacono Data
Note: Data are from applicants to the class of 2019 using Professor Arcidiacono’s expanded sample. Harvard converts applicant GPAs to a
35–80 scale.

Admitted students have strong academic credentials

Source: Arcidiacono Data
Note: Data are from admitted students to the classes of 2014 – 2019 using Professor Arcidiacono’s expanded sample.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 15

3.2. Harvard seeks candidates with a wide range of skills beyond academic achievement
33. Given the extraordinary academic credentials of the Harvard applicant pool each year, the
key question for any statistical analysis of the admissions process (and for assessing SFFA’s
analyses) is: What other characteristics does Harvard evaluate when trying to differentiate among
academically capable students, and how scarce are those characteristics in the applicant pool relative
to the abundance of academic credentials? In this sub-section, I summarize testimony and documents
from Harvard that detail the characteristics it seeks in individual applicants, as well as the broader
diversity in life experiences, perspectives, and interests it seeks for each class as a whole.
3.2.1. Harvard’s whole-person evaluation relies on an “expansive view of excellence,” and seeks to identify
a wide variety of “distinguishing excellences”
34. The guiding principle of Harvard’s admissions process, as I understand it, is to evaluate
each applicant as a whole person, not just in terms of her academic qualifications but in terms of all
other attributes. Documents from Harvard indicate that a central goal of Harvard’s whole-person
evaluation process is an assessment of each applicant’s potential to contribute in various ways to
Harvard’s educational environment and campus community. This process requires a careful
assessment of the aspects of each applicant that distinguish her from other applicants, as well as an
assessment of the context in which the applicant’s achievements occurred, such as the availability of
opportunities to the applicant and the difficulty of the challenges the applicant has faced. Importantly,
documents indicate that academic strength on its own is generally not sufficient to distinguish an
applicant. In fact, Harvard’s 2014 – 2015 Interviewer Handbook (“Interviewer Handbook”) notes that
10

35. The Interviewer Handbook summarizes what Harvard refers to as its
as follows:

10

Interviewer Handbook, 2014 – 2015, HARV00001392 – 1438 (“Interviewer Handbook”) at HARV00001401. Other
documents from Harvard support this account of the admissions process. For example, in a presentation given to guidance
counselors at schools in the Sarasota, Florida area, Harvard admissions officer Kanoe Williams explained that test scores
are just a “small piece” of Harvard’s whole-person evaluation; that, “in general, we can tell pretty quickly if a student will
be an academic fit for our school”; and that “the lengthier part of the conversation typically focuses on intangibles, the
qualitative pieces” (Sarasota Presentation, “KLW - Sarasota Presentation,” HARV00013561 – 65 at HARV00013563 –
64).

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 16

11

36. The Interviewer Handbook then goes on to list a variety of examples of
that admissions officers look for when reviewing application files:
•

•

11

Interviewer Handbook at HARV00001400 – 01.
Deposition testimony indicates that the personal essay is also a key factor in evaluating personal qualities. See, for
example, Deposition of Roger Banks, May 4, 2017 (“Banks Deposition”), pp. 79–80 (“Q. And for each of those
categories, can you tell me how they were assigned a numerical score?...[A] Extracurricularly, quality of achievement,
strength of performance in any particular domain, personal qualities, some grasp of the candidate’s personality, interest in
other people, cooperation with others, a sense of responsibility as gleaned from teacher recommendations, personal
interview, personal essay, et cetera. Q. Okay. So for the last category, the—the main inputs you would look at were
recommendations, interview, and anything else? A. The candidate’s essay.”); Deposition of Brock Walsh, June 28, 2017
(“Walsh Deposition”), p. 60 (“Q How would you calculate that score?…[A.] I would like to take into consideration
whatever relevant information I had were that his essay, her essay, her interview, and the opinions about that applicant as
expressed by others.”); Deposition of Tia Ray, June 7, 2017 (“Ray Deposition”), pp. 21–22 (“Q. What are the materials
that you use—materials or considerations that go into determining this person’s score?…A. For example, content in
recommendation letters, personal essays.”).
12

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 17

•

•

•

•

•

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 18

13

3.2.2. Harvard evaluates applicants’ distinguishing excellences within the context of their full life
experience, including their high school, community, family, and other factors
37. Harvard’s assessment of each applicant’s overall qualifications and distinguishing
excellences takes into account the full context of the applicant’s life experience. My understanding is
that Harvard seeks to understand the opportunities and challenges each applicant has faced so that it
can better evaluate each applicant’s achievements and potential to contribute to Harvard. For
example, William Fitzsimmons, Harvard’s Dean of Admissions and Financial Aid, testified that the
context of each high school is particularly important when evaluating the qualifications of any given
applicant:
Given the fact that we want to understand as completely as possible
what the … applicant has accomplished both in school, out of school,
you know, throughout his or her life, getting to know the school, the
opportunities within the school, academically, extracurricularly, and in
other ways, what they might learn from fellow students, all the usual
things that you might look for in a college that would be of interest. And
also is interesting for the—helpful for readers to understand which
courses might be tougher than others, things of that sort, the full
context.14
38. Marlyn McGrath, Director of Admissions, also testified that the Admission Committee’s
assessment of the context of each applicant’s family life and community is crucial to the evaluation
of her achievements:
The most important thing to say is that when an applicant has applied,
each applicant is really considered as an individual, including—whose
candidacy will always include, generally include, many factors, family
13
14

Interviewer Handbook at HARV00001401 – 02.
Deposition of William Fitzsimmons, August 3, 2017 (“Fitzsimmons Deposition”), pp. 233–234.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 19

background, which will include whatever we know of race, whatever
else we know about family circumstances and education, whatever we
can know about the nature of the school and the kind of community the
student grew up in. Those context features, those features of the
student’s setting are always important to us in imagining how well he’s
achieved in the circumstances that he started with to us as a candidate.15
3.2.3. Documents from Harvard identify specific examples of qualifications that help applicants distinguish
themselves from others
39. To help train admissions officers and alumni interviewers to identify the types of
detailed above, as well as how to evaluate each candidate’s
accomplishments in context, Harvard maintains a Casebook and Casebook Discussion Guide that
highlight examples based on actual application files.16 The discussion guide aims to highlight

17

40. Below are a variety of examples from applications in the Casebook that illustrate the wide
variety of factors Harvard considers in order to distinguish among the many academically strong
candidates in its pool. These factors include, for example, personal qualities like intellectual
arrogance or social charm, economic resources and family hardship, personal essays and interviews,
artistic qualities, maturity and ability to balance multiple commitments, and the degree of parental
involvement:
•

15

Deposition of Marlyn McGrath, Volume I, June 18, 2015 (“McGrath Deposition 2015”), pp. 231–232.
2012 Casebook, HARV00000212 – 321 (“Casebook”); Discussion Guide to the 2012 Casebook, HARV00018164 –
176 (“Casebook Discussion Guide”).
17
Casebook Discussion Guide at HARV00018165.
16

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 20

•

•

•

•

•

•

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 21

18

3.2.4. Harvard seeks diversity of life experience and perspectives for each class on numerous dimensions
41. As noted above, my understanding is that Harvard seeks to admit not just a set of
individuals with distinguishing excellences, but also a class that includes individuals with a wide
range of life experiences and perspectives.
42. For example, the 2016 Report of the Committee to Study the Importance of Student Body
Diversity, chaired by Dean of Harvard College Rakesh Khurana (“Khurana Report”), states:
The mission of Harvard College is to educate the citizenry and citizen
leaders for our society. We take this mission very seriously and firmly
believe it is accomplished through the transformative power of a liberal
arts and sciences education. That transformation begins in the classroom
with exposure to new ideas, new ways of understanding and new ways
of knowing. It is further fostered through a diverse residential
environment where our students live with peers who are studying
different subjects, who come from different walks of life, and have
different identities. This exposure to difference not only deepens a
student’s intellectual transformation, but also creates the conditions for a
social transformation as students begin to question who they are and
how they relate to others.19
43. One form of diversity that Harvard seeks is racial diversity. For example, President Faust
testified: “It’s important that we have a class that represents diversity along a number of dimensions,
and race is one of those dimensions. Economic status is another. Artistic ability is another. Life
experience is another. Interest in a variety of fields that we represent is another.”20
44. Dean Fitzsimmons described how the Admissions Committee considers an applicant’s
self-reported race as one among many factors as it seeks to admit a diverse class:

Casebook Discussion Guide at HARV00018165 – 169, HARV00018174 – 175.
“Report of the Committee to Study the Importance of Student Body Diversity,” HARV00008048 – 69 (“Khurana
Report”) at HARV00008048.
20
Deposition of Catherine Drew Gilpin Faust, March 10, 2017 (“Faust Deposition”), p. 196.
18
19

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 22

We know that race is one factor among many as we review each
application…There are students who might write an essay on how
formative and important race was. There are students who might not
present themselves in such a way. But as one were to look at the
application in its entirety, you could come to the conclusion that race
certainly may have been a factor in their person’s life and may help that
person be a better educator of others during college and beyond. Each
application is different, one from the next.21
3.3. Harvard’s decision process is labor-intensive and seeks to understand the full context of each
applicant’s high school achievements
45. Based on my review of deposition testimony and documents produced in this matter, I
understand that, to implement its whole-person assessment of each applicant, Harvard has
implemented a multi-stage decision process with input from a large team of admissions officers.22
Dean Fitzsimmons has described this as a “rigorous comparative process.”23
46. The Admissions Committee is divided by geographic region into twenty subcommittees,
known as dockets.24 Each subcommittee normally includes four to five members and a chairperson,
who are collectively responsible for the initial evaluation of all candidates from the geographic area.25
Each member of a subcommittee is responsible for performing the initial read of all applications from
a set of high schools on the docket. My understanding is that admissions officers often sit on multiple
subcommittees. The admissions officer who conducts the first read of a given application (the “first

21

Fitzsimmons Deposition, pp. 87–88.
Applicants have the option to apply “Early Action” to Harvard. Early Action applications are due in November, and if
an applicant applies early to Harvard, he may not apply to any other private university’s Early Action or Early Decision
program. Offers of admission to Early Action candidates are announced in December, and are non-binding (that is, an
applicant offered Early Action admission may still apply to other universities in the Regular Decision cycle). Early Action
applicants who are not accepted in December are either denied admission or “deferred” – that is, shifted into the Regular
Decision pool and reconsidered during the Regular Decision admissions cycle. I understand that the subcommittee and
full committee processes for Early Action applicants are primarily the same as described above for Regular Decision, but
with far fewer applications (Harvard College, “Restrictive Early Action,” available at
https://college.harvard.edu/admissions/apply/application-timeline/restrictive-early-action, accessed August 14, 2017).
23
William Fitzsimmons, “Guidance Office: Answers From Harvard’s Dean, Part 1,” New York Times, September 10,
2009, available at https://thechoice.blogs.nytimes.com/2009/09/10/harvarddean-part1/, accessed November 10, 2017.
24
Two of the twenty dockets (U and V) are comprised entirely of applicants from international high schools.
25
William Fitzsimmons, “Guidance Office: Answers From Harvard’s Dean, Part 1,” New York Times, September 10,
2009, available at https://thechoice.blogs.nytimes.com/2009/09/10/harvarddean-part1/, accessed November 10, 2017.
22

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 23

reader”) can choose to pass the application on to the subcommittee chair for review if the first reader
believes that the application merits further consideration.26
47. I understand that admissions officers focus on specific high schools in their geographic
regions, gain detailed knowledge of those high schools, and rely on that knowledge when evaluating
applications.27 In particular, I understand that admissions officers rely on such knowledge to better
evaluate candidates within the context of the academic and non-academic opportunities and
challenges that they have encountered at their particular high schools.28 As I discuss below,
accounting for high school context in a statistical model of the admissions process is critical because
it is one of the important ways in which admissions officers distinguish among candidates.
48. Once all applications from a particular docket have been reviewed, the subcommittee for
that docket meets to discuss the applications. My understanding is that during this process, the first
reader summarizes the strength of the applications he or she has read. Subcommittee members
discuss applications, and then vote on each application to recommend an action to the full
Committee. The degree of support expressed for applicants is noted to allow for comparisons with
applicants from other subcommittees.29 The full Admissions Committee then meets to discuss the
candidates recommended by each subcommittee. For Regular Decision applicants, full committee
meetings take place over the course of approximately two weeks during March.30
49. My understanding is that during the full committee process, the first reader, or area
26

Deposition of Caroline A. Weaver, Volume II, March 6, 2017 (“Weaver Deposition, Volume II”), p. 221 (“If I read an
application and thought that it was a strong application, I would pass it to the chair of the docket.”).
27
Fitzsimmons Deposition, p. 233 (“The beginning piece of the evaluation, you know, would be as, for example, if I
covered Chicago, that I would typically be the first reader of an application from that area. Q. And, in fact, the readers
within a particular docket are divided up by high schools within the docket? A. Yes. Q. So the same reader is supposed to
read all the applications from a particular school? A. Yes. Q. Is that done so that there's better understanding of the way
the school works and the level of classes and information that is going to apply to all applicants? … A. That’s certainly
one of the reasons.”).
28
Fitzsimmons Deposition, pp. 233–234 (“Q. Is that done so that there’s better understanding of the way the school
works and the level of classes and information that is going to apply to all applicants? … A. That’s certainly one of the
reasons. There are others. Q. What are the others? A. Given the fact that we want to understand as completely as possible
what the applica—what the applicant has accomplished both in school, out of school, you know, throughout his or her
life, getting to know the school, the opportunities within the school, academically, extracurricularly, and in other ways,
what they might learn from fellow students, all the usual things that you might look for in a college that would be of
interest. And also is interesting for the—helpful for readers to understand which courses might be tougher than others,
things of that sort, the full context.”).
29
William Fitzsimmons, “Guidance Office: Answers From Harvard’s Dean, Part 1,” New York Times, September 10,
2009, available at https://thechoice.blogs.nytimes.com/2009/09/10/harvarddean-part1/, accessed November 10, 2017.
30
Admissions Calendar 2013 – 2014, HARV00031933.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 24

person, for an application generally presents the applicant’s file to the full Committee, and may
choose to project portions of the application on a screen during the discussion so that the Committee
can review important components of the application.31 For example, deposition testimony indicates
that the admissions officer presenting the case might use excerpts of visual art or music submissions
or academic papers to highlight an applicant’s skills,32 and that discussions in subcommittee or in full
Committee on a single applicant may range in length up to a half hour or more.33 The full Committee
compares all candidates across all subcommittees.34
50. According to Dean Fitzsimmons, “[t]his rigorous comparative process strives to be
deliberate, meticulous, and fair. It is labor intensive, but it permits extraordinary flexibility and the
possibility of changing decisions virtually until the day the Admissions Committee mails them.”35
3.4. Harvard’s ratings reflect important and otherwise unobservable information about the academic
and non-academic qualifications of applicants
51. To help quantify and formalize the evaluation of each applicant by the Admissions
Committee, Harvard employs a numeric rating system. Each admissions officer who reviews an
application rates the applicant on four key dimensions: academic, extracurricular, athletic, and

31

Deposition of Chris Looby, June 30, 2017 (“Looby Deposition”), pp. 33–34 (“Q. Do you ever put a summary sheet on
a projection screen? A. Yes, we do.”).
32
Deposition of Roger Banks, May 4, 2017 (“Banks Deposition”), pp. 197–198 (“A. The area person would begin with
an overall summary of the case, its significant features, academically and extracurricularly, arguments to admit, and
proceed to point the committee toward evidence to support those arguments. Q. Would the members have any other
materials that they’re looking at during that conversation, or is it just what’s presented here? A. It would be what’s
presented here in addition to supplemental information, music tapes, visual art supplements, academic papers, things of
that kind.”).
33
Fitzsimmons Deposition, p. 157 (“But, again, there’s no way to, you know, when 40 people are listening in some cases
for half an hour or more to a single application and discussing that application, exactly why they would choose to admit
that applicant—just impossible to quantify that kind of thing.”).
34
Fitzsimmons Deposition, pp. 297–298 (“And so, in the end, all of those students are—have to be compared against all
of the other people from all the other dockets, and lots of times there’s new information available. You know, there could
be any number of new pieces of information, new interview or whatever, and that might make for a different case. So
every one ultimately gets compared to everyone else in the same process that I have mentioned earlier today, where you
would literally—if you were, say, the area person for a candidate from a school, there would be a docket that people could
look at but then all the information about that applicant would have to go up on the screen and you would have to make
your argument in front of the full committee.”).
35
William Fitzsimmons, “Guidance Office: Answers From Harvard’s Dean, Part 1,” New York Times, September 10,
2009, available at https://thechoice.blogs.nytimes.com/2009/09/10/harvarddean-part1/, accessed November 10, 2017.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 25

personal.36 These are referred to as “profile ratings.” Admissions officers also assign numerical
ratings to the applicant’s “school support”—that is, recommendation letters submitted by high school
teachers and guidance counselors.37 Applicants who receive alumni interviews also receive ratings
from their interviewers, and some applicants may receive additional ratings from interviews by
admissions staff.38 Applicants who submit recordings of musical performances may also receive a
numerical rating assigned by a member of Harvard’s music faculty.39

53. Admissions officers and alumni interviewers also assign applicants an overall rating.42
Deposition testimony indicates that the overall rating (a) takes into account the profile ratings but is
not a formulaic summation or average of those ratings, and (b) can reflect other aspects of an
application that the reviewer considered but that are not captured in the profile ratings (including
race).43 I understand that the numerical ratings in the database may not include certain other
36

These ratings are generally assigned early in the application-reading process, so they do not always reflect
information—such as a faculty evaluation of an applicant’s academic work, or an alumni interview—that may arrive later
on (2018 Reading Procedures at HARV00015414 – 15, HARV00015423 – 24).
37
2018 Reading Procedures at HARV00015416.
38
Interviewer Handbook at HARV00001418; Interview Information Sheet Class of 2017, HARV00000008 – 09 at
HARV00000009; Deposition of Sarah Donahue, June 6, 2017 (“Donahue Deposition”), pp. 193–195 (“Q. … Do the
alumni interviewers themselves assign scores for the applicants which they interview? A. Yes. Q. And is that also on the
four-point scale or the four-number scale? A. Yes. … Q. When there are staff interviews, does the staff assign numbers in
the same way that the alumni interviewers do? … A. They are the same two categories.”).
39
2018 Reading Procedures at HARV00015424.
40
2018 Reading Procedures at HARV00015414 – 16.
41
2018 Reading Procedures at HARV00015415 (“Extracurricular, Community Employment, Family Commitments …
5. Substantial activity outside of conventional EC participation such as family commitments or term-time work…”).
42
2018 Reading Procedures at HARV00015414; Interviewer Handbook at HARV00001429; Interview Information Sheet
Class of 2017, HARV00000008 – 09.
43
Fitzsimmons Deposition, pp. 249–250; McGrath Deposition 2015, pp. 172–173; Deposition of Lucerito Ortiz, June 14,
2017 (“Ortiz Deposition”), pp. 28–29; Deposition of Kaitlin Howrigan, June 20, 2017 (“Howrigan Deposition”), pp. 32–
33; Deposition of Brock Walsh, June 28, 2017 (“Walsh Deposition”), pp. 61, 66–67. For testimony addressing how race
may be taken into account as one of many factors considered when assigning an overall rating, see Ray Deposition, pp.
27–28 (“Q. Is race taken into account when you give a student an overall rating? A. It depends. … Q. How so? … A. On
the individual case and the individual admissions officer,” and “Q. Why is it different—why do you take race into account
in the overall rating but in none of the other ratings? ... A. It depends on the individual case. And we may take it into

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 26

assessments that the Admissions Committee may receive during the course of the admissions
process—for example, evaluations that Harvard faculty members may provide of academic work that
an applicant has submitted.44
54. Each rating is designed to capture numerous characteristics of the applicant that Harvard
values, many of which extend beyond easily quantifiable measures like test scores. For example,
documents and testimony in this case reveal that the academic rating can reflect not only the
applicant’s grades and test scores but also the admissions officer’s knowledge of the applicant’s high
school (and thus ability to place in context the applicant’s academic accomplishments, given the
applicant’s opportunities), as well as the officer’s knowledge of the strength of the candidate’s high
school curriculum, appraisals of the candidate’s academic work by Harvard faculty (to the extent
such appraisals are received before the academic rating is assigned), and the candidate’s receipt of
academic honors or awards.45 It may also reflect the applicant’s writing skills.46 The extracurricular
rating, likewise, reflects not only the number of extracurricular activities in which an applicant has
participated and the number of hours the applicant has devoted to those activities, but also the nature
of the applicant’s activities, whether the applicant has held leadership roles, and whether the activities
are highly selective.47
55. A written set of “Reading Procedures” summarizes the protocols that admission officers
are to follow when reviewing an application and sets forth
that guide how
admissions officers assign profile ratings. The coding guidelines provide standards for when to assign
each rating. For example, a
48

Only about 100 applicants per year receive a 1 academic rating, despite the
large numbers of applicants with extraordinary GPA and SAT/ACT scores, reflecting the critical
importance of information beyond grades and standardized test scores that the readers incorporate
account in that overall rating to reflect the strength of the case and to provide a slight tip for some students.”); Howrigan
Deposition, pp. 35–36 (“Q. So is your answer yes, as long as you knew the student’s race, you would take it into account
[in assigning the overall rating]? … A. If the student opted to share that information on their application, that was
something that was taken into account, with hundreds of other factors that were being taken into account.”); Weaver
Deposition, Volume II, p. 194 (“Q. How does the applicant’s race factor into the overall score? … A. I wouldn’t say that
it factors in directly. Q. But it does factor in indirectly in instances? … A. An applicant’s race becomes important in cases
where the applicant makes that an important part of their folder, if it’s an important part of their identity and the way they
express themselves in their application.”).
44
Harvard Memo, “RE: Faculty Readings,” November 9, 2013, HARV00009879 – 80.
45
2018 Reading Procedures at HARV00015414; Fitzsimmons Deposition, pp. 240–241; McGrath Deposition 2015, pp.
161–162, 166, 168–169; Banks Deposition, p. 80.
46
Banks Deposition, p. 80 (“A. For academics, … some sense of the student’s writing skills.”).
47
2018 Reading Procedures at HARV00015415; McGrath Deposition 2015, pp. 163, 169–171; Donahue Deposition,
p. 160; Ray Deposition, p. 19.
48
2018 Reading Procedures at HARV00015414.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 27

into the ratings.
56. The importance of the ratings in the decision process can be seen in their correlation with
admissions decisions. Exhibit 4 shows how admission rates vary for applicants with different
combinations of profile ratings. For example, it shows that candidates who are exceptionally strong in
a single dimension (reflected by an academic, athletic, extracurricular, or personal rating of 1 and no
other ratings of 1) and candidates who are multi-dimensional (i.e., have at least three profile ratings
of 2) are admitted to Harvard at rates much higher than those of candidates with no ratings of 1 or 2.
Applicants with an academic rating of 1 and no other ratings of 1 are admitted 68% of the time.
Applicants with an extracurricular, personal, or athletic rating of 1 and no other ratings of 1 also have
high admissions rates (48%, 66%, and 88% respectively). Applicants with a rating of 2 on all four
profile ratings are admitted 68% of the time. By contrast, applicants whose four profile ratings are all
3 or worse have almost no chance of admission to Harvard (0.1%).

Specific combinations of Harvard’s four profile ratings have a large effect on the admission rate
Number of
Applicants

Ratings Combination

Admission Rate

Candidates who Excel on One Dimension
1. Academic rating of 1, no other 1s

663

68%

2. Extracurricular rating of 1, no other 1s

453

48%

41

66%

1,340

88%

9,266

43%

622

68%

55,981

0.1%

3. Personal rating of 1, no other 1s
4. Athletic rating of 1, no other 1s
Multi-Dimensional Candidates
5. Three ratings of 2, one rating of 3 or 4
6. Four ratings of 2
Weaker Candidates
7. No ratings of 1 or 2

Source: Arcidiacono Data
Note: Data are from applicants to the classes of 2014 – 2019 using Professor Arcidiacono’s expanded sample.

57. The ratings also indicate that applicants who are highly rated on non-academic dimensions
are much scarcer than applicants with a high academic rating. Exhibit 5 shows that about 42% of
applicants have an academic rating of 1 or 2, while fewer than 25% of applicants receive a 1 or 2 on
each of the other three profile ratings. Applicants with a rating of 2 or better on at least three
dimensions are even rarer—just 7% of the applicant pool. These data indicate that high ratings on

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 28

non-academic dimensions (and particularly on multiple non-academic dimensions) distinguish
applicants in the pool much more effectively than a high academic rating.

Strong academic ratings are more common than strong extracurricular, athletic, and personal
ratings

Source: Arcidiacono Data
Note: Data are from applicants to the classes of 2014 – 2019 using Professor Arcidiacono’s expanded sample.

58. Another way to see the importance of non-academic dimensions relative to academic
dimensions of excellence is to examine how important each element is in explaining which applicants
are admitted. As discussed more fully below, a statistic called the Pseudo R-Squared (on which Prof.
Arcidiacono relies frequently in his analysis) captures how well a variable or set of variables can
explain outcomes—in this case, admissions decisions. The statistic takes on values from zero to one;
the closer it is to zero for a given model, the less information the variables in that model provide
about admissions decisions, while a value closer to one means the model explains a higher proportion
of the variability in the actual decisions. In Prof. Arcidiacono’s expanded sample, the Pseudo RSquared of a model that includes only the academic rating as a control variable is 0.09, while the
Pseudo R-Squared of models that include each of the three non-academic ratings as the sole control
variables are 0.20 (personal), 0.09 (extracurricular), and 0.08 (athletic), and the Pseudo R-Squared for

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 29

a model that includes all three non-academic ratings as control variables is 0.32.49 In non-technical
terms, this means that non-academic factors (taken together) explain more than three times as much
of the variation in admissions decisions as the academic rating does. That should not be surprising,
since exceptional non-academic qualities are less common in the applicant pool than exceptional
academic qualities and are thus more likely to distinguish applicants from one another.
59. Consistent with the discussion above, Exhibit 6 shows that only 12% of admitted students
are “one-dimensional stars” with a rating of 1 on one dimension but fewer than three ratings of 2 or
better, while 46% are multi-dimensional applicants with three or four ratings of 2 or better, and 31%
have two ratings of 2 and two ratings of 3. These statistics are yet another way to show the value that
Harvard places on applicants who distinguish themselves on multiple dimensions.

The vast majority of admitted students excel in multiple dimensions

Source: Arcidiacono Data
Note: Data are from applicants to the classes of 2014 – 2019 using Professor Arcidiacono's expanded sample. Category 2 also includes five
applicants who received two ratings of 1 and two ratings of 3.

60. One final point about the ratings warrants mention. Prof. Arcidiacono argues that the
athletic rating “has little impact on admissions outside of recruited athletes,”50 and that “once athletes
are taken out, the relationship between the athletic rating and admissions is weak.”51 These assertions
directly contradict both testimony and documents from Harvard, as well as the admissions data.

49

See workpaper.
Arcidiacono Report, p. 5, footnote 5.
51
Arcidiacono Report, p. 24, footnote 31.
50

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 30

61. For example, as noted above, the Interviewer Handbook explicitly notes that athletic
ability can be a
and is
52

This “tip” is not limited to recruited varsity athletes; it also reflects the
value Harvard places on recreational athletics and an applicant’s potential contribution to life in
Harvard’s residential Houses. For example, the Interviewer Handbook notes:

53

The Reading Procedures also note that an athletic rating of 2 typically reflects having a
strong role on at least one high school team, as well as a possible leadership role.54 In other words, an
athletic rating of 2 indicates a substantial role in an athletic extracurricular activity.
62. Harvard’s admissions data confirm the importance of the athletic rating. For example,
applicants with an athletic rating of 2 have an admission rate of 12%. That is substantially higher than
the overall admission rate of approximately 7%, and is the same as the admission rate of applicants
with an academic rating of 2. Further, as shown above, receiving a rating of 2 on all four profile
ratings is associated with an admission rate of 68%, while receiving a rating of 2 on the three nonathletic ratings and a rating of 3 or worse on the athletic rating is associated with an admission rate of
only 48%. This contrast provides further evidence of the incremental importance of an athletic rating
of 2.55
3.5. Prof. Arcidiacono’s statistical model fails to account for numerous dimensions of Harvard’s
admissions process
63. Prof. Arcidiacono’s analysis clearly fails to reflect the complexity of the admissions
process described above.
64. First, although Harvard values academic achievements, academic qualifications are only
one factor in the evaluation of each candidate, and applicants with exceptional academic records are
abundant in the Harvard applicant pool. Harvard’s whole-person evaluation extends beyond test
scores, GPA, and other measures of prior academic achievement.56 Yet Prof. Arcidiacono focuses
overwhelmingly on the relative academic strength of Asian-American applicants. For example, in

52

Interviewer Handbook at HARV00001401.
Interviewer Handbook at HARV00001402.
54
2018 Reading Procedures at HARV00015415.
55
See workpaper.
56
Sarasota Presentation, “KLW - Sarasota Presentation,” HARV00013561 – 65 at HARV00013563.
53

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 31

four of his six regression specifications, Prof. Arcidiacono does not include controls for the three
non-academic ratings (extracurricular, personal, and athletic). Such models are incapable of
accounting for the admissions process detailed above, and shed no useful light on the issues in this
case.
65. Second, as I detail in the next section, it is difficult to quantify and include in a statistical
model many of the non-academic and contextual factors that Harvard’s admissions process values.
That is particularly important for assessing racial disparities in admission because, as I show in the
next section, there are significant racial differences in the non-academic and contextual factors that
are measured in Harvard’s admissions database and that Prof. Arcidiacono chooses not to include in
his model. That suggests there may well also be racial differences in the many other non-academic
factors (like the personal essay) that are not observable in the database and that are important to the
admissions process given the large pool of applicants with extraordinary academic achievements.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 32

4. ACCOUNTING FOR NON-ACADEMIC AND CONTEXTUAL FACTORS IS CRITICAL IN
MODELING HARVARD’S ADMISSIONS PROCESS
66. Before turning to my formal statistical analyses in Sections 5, 6, and 7, in this section I
discuss several facts that Prof. Arcidiacono has overlooked (or misunderstood), that provide
important context for the more technical analysis that follows in the remainder of this report, and that
illustrate the flaws in Prof. Arcidiacono’s arguments.
67. I start by examining the differences in admission rates between Asian-American and
White applicants. One of SFFA’s central claims in this matter is that Asian-American applicants are
admitted at a lower rate than White applicants. As I show below, however, that is not true if one
focuses on applicants who are neither lineage applicants, nor recruited athletes, nor children of
Harvard faculty and staff, nor included on the Dean’s and Director’s interest lists—all categories of
applicants that Prof. Arcidiacono believes should be removed from an analysis of bias. Fully 95% of
applicants fall outside those categories.57 And among that 95% of applicants, Asian-American
applicants are admitted at slightly higher rates than White applicants.
68. I then explain why the data do not support one of the central assumptions of Prof.
Arcidiacono’s analysis—that Asian-American applicants are stronger on all dimensions of quality,
including non-academic characteristics. As I detail below, White applicants are stronger on average
than Asian-American applicants across the three non-academic profile ratings combined, and are
stronger (in aggregate) across all of the non-academic variables that can be observed in the database
and that are included in Prof. Arcidiacono’s model. As noted earlier, the observable measures of nonacademic achievement are also limited: it is clear from the documents and testimony in this case that
Harvard is using other information such as recommendation letters and the applicants’ personal
essays to form its assessments of each candidate’s non-academic strengths. This information is
“missing data” that cannot be observed in the admissions database. If the racial gaps in these missing
data are similar to the racial gaps in the observed measures of non-academic achievement, then Prof.
Arcidiacono’s model is biased in favor of finding an adverse effect of Asian-American ethnicity on
applicants’ probability of admission, since it omits variables that, if included as controls, would
decrease the size of (or eliminate entirely) the estimated negative effect of Asian-American ethnicity.
69. Finally, Prof. Arcidiacono’s model includes very little information to account for the
overall context of each candidate’s application (such as the quality of the applicant’s high school, the
socioeconomic characteristics of the applicant’s high school and neighborhood, and the applicant’s
family background), even though Prof. Arcidiacono had access to data that shed light on all those
factors. Using these data, I highlight a variety of average differences between White and Asian57

See workpaper.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 33

American applicants that I understand to be relevant to Harvard’s whole-person analysis. For
example, White and Asian-American applicants tend to come from different sets of high schools and
different parts of the country, have parents with different occupational backgrounds, and have
different intended careers. All of these factors provide important context for reviewing applications.
4.1. There is no statistically significant difference in admission rates for the vast majority of AsianAmerican and White applicants
70. SFFA’s claim of bias relies heavily on the premise that Asian-American applicants are
admitted at lower rates than White applicants despite having stronger qualifications. But as Prof.
Arcidiacono acknowledges in his report, when exploring whether there is bias against AsianAmerican applicants, it is important to account for the fact that Harvard’s admissions process gives
special consideration (independent of race) to children of Harvard or Radcliffe alumnae or alumni
(referred to as “lineage applicants,” and which Prof. Arcidiacono refers to as “legacy applicants”),
applicants recruited to play a varsity sport at Harvard, and children of Harvard faculty or staff
members. The Dean and Director of Admissions also maintain “interest lists” of applicants; I
understand that there are no particular criteria for inclusion on those lists but that they might include,
for example, applicants that the Dean or Director have encountered at recruiting events, as well as
applicants related to donors to Harvard or lineage applicants.58 Indeed, Prof. Arcidiacono removes
applicants in those categories from what he calls his “baseline” sample, before exploring the question
of bias.59
71. Exhibit 7 shows the admission rates by race, once applicants in the categories noted above
are excluded from the sample. It shows that Asian-American applicants are admitted at a slightly
higher rate than White applicants (though the difference is not statistically significant). Although the
numbers in Exhibit 7 do not settle the question of whether there is bias against Asian-American
applicants (because they do not account for the full set of characteristics of each applicant), the fact
that the difference in admissions rates disappears by controlling for just these factors raises serious
questions about SFFA’s allegations of bias. The remainder of this section explores a variety of other
important factors that differ between White and Asian-American applicants and that, once accounted
for, eliminate the alleged disparity in admission rates.

58

Fitzsimmons Deposition at pp. 264–267, 278.
Prof. Arcidiacono also removes from his baseline sample applicants who apply during the Early Action cycle
(Arcidiacono Report, p. 2). I do not follow that approach here. I understand that the process for evaluating Early Action
applications is the same as that for evaluating Regular Decision applications except that Early Action applications are
evaluated earlier and have the potential to be deferred to the Regular Decision pool.

59

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 34

Admission rates for applicants who are not lineage applicants, athletic recruits, children of
Harvard faculty or staff, or on Dean’s or Director’s Interest List

Source: Arcidiacono Data
Note: Data are from applicants to the classes of 2014 – 2019 in Professor Arcidiacono’s baseline sample with Early Action applicants.

4.2. White applicants have relatively stronger qualifications on non-academic dimensions
72. A central assumption in Prof. Arcidiacono’s analysis is that, because Asian-American
applicants are stronger on academic dimensions, they are also stronger on non-academic
dimensions—including those dimensions not accounted for by his model.60 This assumption leads
Prof. Arcidiacono to focus much of his analysis on academic qualifications, and to conclude that any
difference in admission rates not accounted for by his model must be caused by “bias” against AsianAmerican applicants. As I show in this sub-section, however, a proper interpretation of the available
data indicates that Prof. Arcidiacono’s assumption is incorrect. White applicants are in fact stronger,
on average, on non-academic factors that Harvard values.
73. Exhibit 8 shows that Asian-American applicants tend to have higher academic ratings and
slightly higher extracurricular ratings than White applicants, while White applicants tend to have
higher personal and athletic ratings61 and are more likely to be multi-dimensional (i.e., more likely to
have a rating of 2 or better on at least three of the four profile ratings). Importantly, the average
60

Throughout his report, Prof. Arcidiacono presents a variety of analyses that show how non-academic ratings correlate
with Harvard’s academic index. He does not, however, directly examine whether Asian-American applicants are stronger
than White applicants collectively across all non-academic factors in his model. My analysis in this section explores that
question.
61
As I discuss in Section 5 below, even if one grants Prof. Arcidiacono’s assumption that personal ratings are biased
against Asian-American applicants, his own analysis shows that White applicants still have higher personal ratings even
after statistically eliminating the supposed bias.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 35

difference between Asian-American and White applicants on extracurricular ratings (the one nonacademic rating on which Asian-American applicants perform better than White applicants) is
smaller in magnitude than the average differences in athletic and personal ratings (on which White
applicants perform better than Asian-American applicants).

White and Asian-American applicants excel in different dimensions: percentage of applicants with
ratings of 2 or better

Source: Arcidiacono Data
Note: Data are from applicants to the classes of 2014 – 2019 using Professor Arcidiacono’s expanded sample. Ratings of 2- and above are
classified as “2 or Better” in this analysis. +/- rating designations are available in the data beginning with the class of 2019.

74. Exhibit 9 presents another way to measure the difference between Asian-American
applicants and White applicants on non-academic characteristics—one that accounts for the collective
strength of each applicant across all three non-academic profile ratings. It shows the proportion of
applicants with a given academic rating whose cumulative non-academic rating—that is, the sum of
the extracurricular, athletic, and personal ratings—is seven or less.62 A cumulative non-academic
62

Applicants with athletic or extracurricular ratings of 5 and 6 are excluded from this analysis because those ratings
indicate that there were special circumstances that caused the applicant to have fewer athletic or extracurricular

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 36

rating of seven or less indicates a candidate who is very strong across all three non-academic
dimensions. The cutoff of seven is also highly informative about admissions probabilities: applicants
whose non-academic ratings add up to seven or less have a 38% admission rate, while those with a
higher sum have only a 4% chance of admission.63
75. Exhibit 9 shows that, for a given academic rating, White applicants are much more likely
to have strong non-academic ratings than Asian-American applicants. For example, for applicants
with an academic rating of 1, 25% of White applicants have very strong non-academic ratings,
compared to only 16% of Asian-American applicants (roughly one-third fewer). Similarly, among the
large group of applicants with an academic rating of 2 (representing nearly half of Asian-American
and White applicants), 14% of White applicants, but only 8% of Asian-American applicants, have
very strong non-academic ratings. This gap in non-academic achievement is critically important. As
detailed in Section 3, because academic qualifications are abundant in the applicant pool, it is the
non-academic dimensions that often distinguish academically strong applicants from each other.64
Exhibit 9 shows that, for a given level of academic achievement, White applicants are substantially
more likely to have higher ratings across the three non-academic dimensions taken together.65

accomplishments, such as significant family commitments or a physical disability. Applicants with profile ratings of 7, 8,
or 9 are excluded from this analysis because those are not valid ratings according to the reading procedures (2018
Reading Procedures at HARV00015414 – 15). In my regression analysis, I treat such ratings as missing.
63
See workpaper.
64
Academic research has found that Asian-American high school students are more likely to apply to selective
institutions than White high school students, even controlling for academic qualifications. In other words, even
accounting for academic qualifications, a different sample of Asian-American and White high school students apply to
institutions like Harvard. This differential behavior in the college application process is one possible reason why, on
average, White and Asian-American applicants in the Harvard pool might exhibit different qualifications across the
different dimensions Harvard evaluates. Sandra Black, Kalena Cortes, and Jane Lincove, “Apply Yourself: Racial and
Ethnic Differences in College Application,” NBER Working Paper #21368, 2015; Sandra Black, Kalena Cortes, and Jane
Lincove, “Academic Undermatching of High-Achieving Minority Students: Evidence from Race-Neutral and Holistic
Admissions Policies,” American Economic Review: Papers & Proceedings, 105(5), 2015, pp. 604–610; Amanda Griffith
and Donna Rothstein, “Can’t Get There from Here: The Decision to Apply to a Selective College,” Economics of
Education Review, 28(5), 2009, pp. 620–628; David Card and Alan Krueger, “Would the Elimination of Affirmative
Action Affect Highly Qualified Minority Applicants? Evidence from California and Texas,” Industrial and Labor
Relations Review, 58(3), 2005, pp. 416–434.
65
In Appendix C of his report, Prof. Arcidiacono argues that “Harvard applies the label ‘Standard Strong’
disproportionately to Asian-American applicants” and that “Asian-American applicants who are labeled this way are
substantially more qualified academically than ‘Standard Strong’ applicants from other racial groups.” However, if one
considers strength more broadly (as measured by the sum of all four profile ratings), Asian-American and White
applicants who are labeled “Standard Strong” are equally strong. See workpaper.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 37

For a given academic rating, White applicants tend to have better non-academic ratings than
Asian-American applicants

Source: Arcidiacono Data
Note: Data are from applicants to the classes of 2014 – 2019 in Professor Arcidiacono’s expanded sample.

76. Exhibit 10 presents yet another way to measure the relative strength of White and AsianAmerican applicants on non-academic factors, using Prof. Arcidiacono’s own model (specifically, his
Model 6, with the overall rating excluded). In Table 7.3 of his report, Prof. Arcidiacono constructs an
“admissions index,” attempting to quantitatively summarize the overall qualifications of applicants
based on all of the factors in his model.66 In Exhibit 10, I have reproduced that same analysis but
focusing only on the non-academic factors in his model. That is, I have removed from his admissions
index the effect of the academic rating, grades, and all standardized test scores. The exhibit shows
that, using Prof. Arcidiacono’s own metric, Asian-American applicants are more likely than White
applicants to have weaker non-academic qualifications (i.e. be in deciles 1 to 5), and that White
applicants are more likely than Asian-American applicants to have strong non-academic
qualifications (i.e. be in deciles 9 and 10). The same pattern is observed if I repeat this analysis but
estimate the non-academic admissions index using Prof. Arcidiacono’s Model 5, which excludes the
personal rating.67 In other words, Prof. Arcidiacono’s own models show that White applicants are
stronger than Asian-American applicants on non-academic dimensions, and this finding holds even if
personal ratings (which Prof. Arcidiacono alleges are biased) are excluded from the non-academic
qualifications.

66
67

Arcidiacono Report, p. 68, Table 7.3.
See workpaper.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 38

White applicants rank higher than Asian-American applicants on non-academic admissions index

Source: Arcidiacono Data
Note: Data are from applicants to the classes of 2014 – 2019 in Professor Arcidiacono’s expanded sample. The non-academic admissions
index is constructed in the same fashion as Professor Arcidiacono’s overall admissions index, using his model 6 without the overall rating
to calculate applicants’ probability of admission. Applicants with characteristics that guaranteed rejection or admission were assigned to
the bottom or top decile, respectively. In addition to excluding the effect of race, as Professor Arcidiacono did, I exclude the effects of the
academic rating and academic variables (such as Academic Index, SAT scores, and GPA).

77. As noted above, these facts are critical to the interpretation of Prof. Arcidiacono’s model
and SFFA’s broader claim of bias. Throughout his report, Prof. Arcidiacono argues that, because
Asian-American applicants have stronger academic credentials (on average) than White applicants,
he can safely assume that they are also stronger than White applicants on dimensions of quality—
mostly non-academic—that cannot be measured by his statistical model. If that were true, it would
imply that adding more variables to Prof. Arcidiacono’s model to further control for differences
between White and Asian-American applicants would only increase the estimated negative effect of
Asian-American ethnicity on applicants’ probability of admission. But, as I have shown above, Prof.
Arcidiacono’s assumption is demonstrably incorrect.
78. In fact, although Asian-American applicants are stronger than White applicants (on
average) on quantifiable measures of academic performance, they are (on average) less strong than
White applicants on observable non-academic measures (Harvard’s ratings and Prof. Arcidiacono’s
admissions index). Because non-academic factors are harder to quantify and include in the model
than academic factors, any statistical model of the Harvard admissions process is therefore more
likely to have more missing information about non-academic factors than about academic factors.
And if the racial gap in the missing non-academic factors is similar to the racial gap in the measured
non-academic factors (i.e., with Asian-American applicants having less strong qualifications than
White applicants), then a statistical model of the admissions process will be predisposed to find a
negative effect of Asian-American ethnicity on applicants’ likelihood of admission, even though the

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 39

racial disparity in admission rates may actually be due to racial differences in the missing nonacademic information.
4.3. Prof. Arcidiacono’s model excludes available measures of life circumstance and context
79. In the remainder of this section, I highlight a set of important contextual factors—that is,
factors that reflect the wide range of applicant characteristics that may inform admissions officers’
evaluation of each application—that Prof. Arcidiacono excludes from his model and that differ, on
average, between Asian-American applicants and White applicants. As I will show in Section 5, these
contextual factors help explain the disparity in admission rates between Asian-American and White
applicants, and when added to Prof. Arcidiacono’s model lead to the conclusion that there is no
statistically significant negative effect of Asian-American ethnicity on applicants’ likelihood of
admission.
80. As detailed above in Section 3, Harvard seeks to assess the quality of each applicant, in
academic and non-academic respects, in light of the context provided by any available information
about the challenges the applicant has faced, the resources at her disposal, and the opportunities she
has (or has not) encountered. A major limitation of Prof. Arcidiacono’s model is that it includes very
few variables to account for these contextual factors. For example, Prof. Arcidiacono includes only a
very limited set of socioeconomic variables, in addition to control variables that account for only
broad differences across types of neighborhoods and high schools, as reflected in high school and
neighborhood “cluster” numbers assigned by a proprietary algorithm of the College Board. Prof.
Arcidiacono does not make use of the more detailed data about each individual high school and
neighborhood that were produced along with the College Board’s “cluster” identifiers and that inform
the College Board cluster assignments.68 For example, his model includes controls for 29 high school
clusters, yet there are more than 14,000 high schools represented in the Harvard applicant pool.69
Using the more detailed high school and neighborhood characteristics data can add meaningful
information to the model.70 As I show in this section, this modeling decision by Prof. Arcidiacono is
problematic because Asian-American and White applicants (in aggregate) come from different sets of
high schools and different regions of the country, have different career goals, and have different

68

Prof. Arcidiacono’s model includes the following socioeconomic controls: an indicator of whether the admission
officer believed the applicant to be “disadvantaged,” an indicator of whether the applicant applied for a waiver of the
application fee, an indicator of whether the applicant applied for financial aid, an indicator of whether the applicant is in
the first generation of his family to attend college, and indicators of the applicant’s mother’s and father’s educational
attainment.
69
See workpaper.
70
Additionally, Prof. Arcidiacono excludes a variable in the Harvard data indicating the type of high school an applicant
attended (Archdiocese, Public, or Private). I include this variable in my models.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 40

family backgrounds (such as parental occupations).
4.3.1. Detailed controls for differences across high schools and neighborhoods
81. As shown in Exhibit 11, Asian-American and White applicants come from very different
sets of high schools. Nearly half of the high schools represented in the applicant pool have either
White applicants but no Asian-American applicants or Asian-American applicants but no White
applicants. This means that, without better controls in the model for high school characteristics, Prof.
Arcidiacono is missing an important difference between the two groups.71

Asian-American and White applicants come from different high schools

Source: Arcidiacono Data
Note: Sample consists of Professor Arcidiacono’s expanded sample.

71

For example, high school characteristics include the high school’s mean SAT score or the percentage of students in the
high school who require financial aid for college. For a complete list of high school and neighborhood characteristics
included in my model, see Appendix E. The College Board high school and neighborhood data report many high school
and neighborhood characteristics based upon only the set of students from a given high school who take the SAT.
Because the SAT is more common in some areas and the ACT in others, in my model I allow for these variables to have
different effects in states where the SAT is more common than in states where the ACT is more common.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 41

82. Asian-American and White applicants also come from different geographic regions.
Asian-American applicants are more concentrated on the East and West coasts and in major cities. In
fact, approximately 29% of all Asian-American applicants come from California dockets (dockets A,
C, and Z), as compared to only 14% of White applicants.72 Exhibit 12 highlights these differences by
showing a map of all locations of Asian-American and White applicants’ high schools. The blue dots
indicate locations with White applicants but no Asian-American applicants (during the 2014 – 2019
admissions cycles). As is clear from Exhibit 12, there are a large number of blue dots in the central
and rural areas of the U.S.

White applicants are more dispersed across the U.S. and rural areas

Source: Augmented Arcidiacono Data
Note: Sample consists of Prof. Arcidiacono’s extended dataset for the classes of 2014 – 2019. Each blue dot represents the city of a high
school from which at least one White applicant applied. Each red dot represents the city of a high school from which at least one AsianAmerican applicant and one White applicant applied.

83. Although Prof. Arcidiacono controls for an applicant’s admissions docket (i.e., broad
geographic region), he does not control for the much more detailed neighborhood attributes available
in the College Board data, such as the median income of the neighborhood (defined as a census tract
or collection of census tracts) or the proportion of students in a neighborhood who apply to an out-ofstate college. Nor does he control for whether the applicant attends high school in a rural area or the
72

See workpaper.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 42

type of high school (public, private, or Archdiocese).
4.3.2. Other proxies for life experience, opportunities, and ambitions
84. In addition to ignoring the detailed available data on applicants’ high schools and
neighborhoods, Prof. Arcidiacono also fails to include in his model several available variables that
reflect differences in applicants’ family background and life goals.
85. For example, Prof. Arcidiacono ignores data on parental occupations, a critical measure of
family background. As noted above, family background provides important context for each
applicant’s achievements. Exhibit 64 and Exhibit 65 (Appendix C) show that the parents of AsianAmerican and White applicants tend to have different types of occupations.73 33% of fathers and 16%
of mothers of Asian-American applicants work in the fields of “Computer and Mathematical,” “Life,
Physical, Social Science,” or “Architecture and Engineering,” while only 16% and 5% (respectively)
of fathers and mothers of White applicants work in those fields.
86. Such differences can reflect not just differences in a family’s economic prosperity but also
differences in applicants’ life experiences. For example, if the son of a professional writer and the son
of a police officer display talent in writing, Harvard might regard the latter’s talent as more
impressive than the former’s. The same might be true of the daughter of professional scientists and
the daughter of factory workers, both of whom exhibit talent in a scientific field. In fact, one of the
examples from Harvard’s casebook (discussed above in Section 3.2) specifically notes parental
occupation as relevant context for evaluating her achievements:

87. Prof. Arcidiacono also excludes from his model other available data on applicants’ family
background, including whether an applicant’s mother or father is deceased, whether one or both of
the applicant’s parents attended an Ivy League university, whether the applicant was born outside the
United States, whether the applicant has lived outside the United States, whether the applicant is a
permanent resident of the United States, and the hours an applicant spent working at a job.74

73

In the Harvard database, applicants report parental occupations using either a Bureau of Labor Statistics (BLS) code or
a Common Application code. Reported parental occupation codes are harmonized by mapping Common Application
codes to major and minor groups in the BLS’ Standard Occupational Classification System. Major and minor groups are
then combined into broad occupational categories.
74
Information on how much time an applicant spent working at a job and on whether an applicant was born outside the
United States or lived outside the United States is available only for applicants to the classes of 2017 to 2019 and so can
be included in my year-by-year model but not in Prof. Arcidiacono’s pooled model.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 43

88. Finally, Prof. Arcidiacono does not include a variable for intended career in his model.
Exhibit 13 shows the differences in intended careers between Asian-American and White applicants.
Asian-American applicants are much more likely to intend to pursue a career in medicine or health,
while White applicants are much more likely to intend to pursue careers in the arts, communications,
design, social service, government, or law. The difference in the intended career of medicine or
health is particularly stark—White applicants are 37% less likely than Asian-American applicants to
pursue this intended career, an intended career with the lowest admission rate (5%). As detailed
above in Section 3.2, an applicant’s future plans and fields of interest can be critical to the assessment
of how the applicant will contribute to the Harvard community both inside and outside the
classroom.75 For example, the Casebook Discussion Guide notes the following about one candidate:

76

White and Asian-American applicants have different intended careers

Source: Augmented Arcidiacono Data
Note: Data are from applicants to the classes of 2014 - 2019 in Professor Arcidiacono’s expanded sample. The “Other” category includes
applicants whose intended careers are academic, library, religion, trade, other, or unknown. Categories for intended careers can vary year
to year.

75

Another factor that reveals an applicant’s interests is the type of extracurricular activities on which the applicant has
focused in high school. Prof. Arcidiacono does not include any measure of the type of extracurricular activities in his
model. As shown in Appendix D, there are significant differences across racial groups in applicants’ primary activities
(defined as those listed first or second on the application). Applicants are instructed to list the activities most important to
them first on the Common Application. Information on activities is available in Harvard’s data only for applicants to the
classes of 2017 to 2019.
76
Casebook Discussion Guide at HARV0018166.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 44

89. Prof. Arcidiacono’s decision to ignore available information related to non-academic
considerations, including contextual factors, is particularly curious because his own regression
models indicate that such variables can help explain differences in admission rates between AsianAmerican and White applicants. For example, as he adds measures of academic achievement to his
model (moving from Model 1 to Model 2), the estimated negative association between AsianAmerican ethnicity and likelihood of admission increases. But as he adds more variables that capture
the context of each candidate’s application—such as broad high school and neighborhood
demographics, and ratings that capture non-academic characteristics of the applicant—the estimated
negative effect of Asian-American ethnicity shrinks substantially (Model 4 to Model 6).77
90. In the next section of this report, I show that the same general pattern holds in my model:
As I add to the model the additional non-academic variables discussed in this section, the estimated
negative effect of Asian-American ethnicity on applicants’ likelihood of admission disappears. This
finding is consistent with the hypothesis that what Prof. Arcidiacono labels a “bias” against AsianAmerican applicants in fact reflects not racial discrimination but differences in non-academic factors
that Harvard considers in its whole-person evaluation.

77

Arcidiacono Report, Appendix B, Table B.7.2.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 45

5. A MORE COMPLETE STATISTICAL MODEL SHOWS NO EVIDENCE OF BIAS AGAINST
ASIAN-AMERICAN APPLICANTS
91. As detailed in Sections 3 and 4 above, because Harvard’s whole-person admissions
process heavily considers non-academic and contextual factors that are often hard to measure, a
statistical model that can reliably estimate the effect of race on Harvard’s admissions decisions
should seek to include as much reliable information about such factors as possible. In this section, I
develop such a statistical model by starting with Prof. Arcidiacono’s model and then expanding his
set of control variables to include a richer set of characteristics that he did not include in his model,
and that more fully capture the many factors that Harvard considers in its process. I further revise the
model by allowing the coefficients it estimates for different control variables, which reflect the
effects of different applicant attributes on the probability of admission, to vary from year to year. I
then use this more complete model in the remainder of this report to address several questions at issue
in this matter.
92. The first question I examine is whether the alleged negative association between AsianAmerican ethnicity and applicants’ likelihood of admission persists when more information is
included in the model. I find that it does not. When more variables are added to the model to capture
differences in key contextual factors (high school, neighborhood, and family background), and when
the model is estimated year-by-year to account for differences in the admissions process from year to
year, the alleged negative effect of Asian-American ethnicity disappears and the predictive accuracy
of my model increases.
93. I then turn to a second question in Section 6: to what extent does an applicant’s race or
ethnicity matter in the admissions process, relative to the many other factors Harvard considers in its
whole-person analysis? While I find that race is significantly associated with the likelihood of
admission for some applicants, the role it plays is less significant than that of other factors included in
my model, as well as that of factors not observable in the model.
94. Before delving into the details of my analysis, I first discuss several important
methodological issues that arise when building an admissions model, with a focus on differences
between my approach and Prof. Arcidiacono’s.
5.1. Important differences between Prof. Arcidiacono’s methodology and mine
95. Prof. Arcidiacono uses a statistical model known as a multivariate logit regression to
estimate the relationship between race and admissions outcomes, while controlling for a variety of

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 46

factors that Harvard considers in admission decisions.78 The use of a multivariate logit model makes
sense. Multivariate regression analysis is a widely accepted and common statistical technique in both
academia and litigation.79 Courts have relied on multivariate regression analysis in a variety of
discrimination matters. In fact, the Reference Guide for Scientific Evidence dedicates an entire
chapter to multivariate regression analysis, including applications to questions of discrimination.80 A
logit model is a type of multivariate regression model that is appropriate where, as here, the outcome
of interest—in this case admission to Harvard—is binary, taking values of either zero (not admitted)
or one (admitted).
96. Even though I agree with Prof. Arcidiacono’s general approach, I disagree with several of
the specific modeling decisions he makes when building his model. In the remainder of this section, I
discuss these methodological decisions and explain why Prof. Arcidiacono and I reach different
conclusions.
5.1.1. Inclusion of additional control variables
97. A basic tenet of econometric research is that the selection of control variables should be
informed by the research question at hand and the specific outcome that is being modeled.81 Thus, the
first step in my analysis is to add to Prof. Arcidiacono’s fullest models (Models 5 and 6) any
variables missing from his models that Harvard considers in the admissions process.
98. As detailed in Sections 3 and 4 above, the most important feature of Harvard’s decision
78

William H. Greene, Econometric Analysis (Pearson, 2008), pp. 773–774 (“The probit and logit models are still the most
common frameworks used in econometric applications.”); Kenneth E. Train, Discrete Choice Methods with Simulation
(The Cambridge University Press, 2009), p. 34 (“By far the easiest and most widely used discrete choice model is logit.”).
79
James H. Stock and Mark W. Watson, Introduction to Econometrics (Pearson, 2015), p. 189 (“The multiple regression
model … permits estimating the effect … of changing one variable while holding the other regressors constant… provides
a way to isolate the effect.”); William H. Greene, Econometric Analysis (Pearson, 2008), pp. 8–10 (“The linear regression
model is the single most useful tool in the econometrician’s toolkit. The multiple linear regression model is used to study
the relationship between a dependent variable and one or more independent variables. One of the most useful aspects of
the multiple regression model is its ability to identify the independent effects of a set of variables on a dependent
variable.”).
80
Daniel L. Rubinfeld, Reference Manual on Scientific Evidence: Third Edition (The National Academies Press, 2011),
pp. 305–307 (“Regression analysis has been used most frequently in cases of sex and race discrimination, antitrust
violations, and cases involving class certification.”).
81
James H. Stock and Mark W. Watson, Introduction to Econometrics (Pearson, 2015), pp. 232–234 (“The starting point
for choosing a regression specification is thinking through the possible sources of omitted variable bias… A control
variable is not the object of interest in the study; rather it is a regressor included to hold constant factors that, if neglected,
could lead the estimated causal effect of interest to suffer from omitted variable bias.”).

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 47

process that Prof. Arcidiacono’s model does not account for is the substantial consideration Harvard
gives to non-academic factors that help distinguish among the large number of academically strong
applicants in its pool, including a wide variety of contextual factors that account for the life
experience and background of each candidate (e.g., her high school, community, and family
background).
99. The first panel in Exhibit 14 shows the variables that Prof. Arcidiacono includes in his
fullest models (Models 5 and 6), while the second panel lists the additional variables I include in my
model. Both sets of variables are organized into several broad groups: race, base controls (a category
that includes personal and financial variables, such as an applicant’s gender, docket, and parents’
education), Harvard profile ratings (academic, extracurricular, personal, and athletic), other ratings
(such as those assigned by admissions officers to recommendation letters from teachers or guidance
counselors, or those assigned by alumni interviewers), measures of academic qualifications, high
school and neighborhood characteristics, and interaction terms (such as interactions of race with
gender that are included in Prof. Arcidiacono’s models).82 As shown, the additional variables that I
add to my model include intended career; staff interview ratings; richer controls for high school and
neighborhood characteristics;83 parents’ occupations; applicant’s hours worked (at a job); controls for
specific combinations of profile ratings, specific combinations of teacher ratings, and specific
combinations of alumni interview ratings; indicators for participation in different types of primary
extracurricular activities; and indicators for having parents who attended an Ivy League college,
having parents who attended Harvard for graduate school, having a mother or father who is deceased,
being a permanent resident of the United States, having been born in the United States, and having
lived outside the United States.84

82

Appendix E provides a complete list of variables used in my model with detailed definitions.
For the College Board high school and neighborhood variables most likely to be missing for applicants in the sample
(neighborhood median income, proportion of neighborhood residents below poverty line, and neighborhood median
housing value), I assign the mean value of the variable to those applicants who are missing data and include an indicator
variable identifying those for whom the mean was assigned. This approach of imputing missing values is analogous to
that used by Prof. Arcidiacono in his report.
84
In my year-by-year models, there is not enough data to estimate separately the effect of some of the specific ratings that
are very rare in the data due to limited sample size. To resolve this problem, rather than include a separate dummy
variable for each individual rating category, I include a dummy variable for each unique combination of the four profile
ratings, each unique combination of the two teacher ratings, and each unique combination of the alumni interviewer
ratings. Unique ratings combinations with fewer than 100 observations are grouped with other ratings combinations such
that the combination is in a group that has an admission rate most similar to that of the combination. I have confirmed that
this approach has no substantive effect on the estimated size of the Asian-American coefficient and yields nearly the same
predictive accuracy as Prof. Arcidiacono’s approach in the pooled model. (See workpaper.) Additionally, as I explain
below, my year-by-year models using this approach more accurately predict Harvard’s admissions decisions than Prof.
83

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 48

100. I also make a series of more technical corrections to Prof. Arcidiacono’s variables and
sample. First, Prof. Arcidiacono includes a number of variables that he interacts with race and gender
in his model.85 An “interaction” variable simply multiplies one variable by another variable, to show
how the presence or absence of the second variable modifies the effect of the first. For example, one
could model the effect of male gender on the likelihood of admission, the effect of Asian-American
ethnicity, and the effect of male gender and Asian-American ethnicity—that is, the extent to which
being male decreases or increases the effect of being Asian American, and vice versa. Since there are
hundreds of potential interactions one could add to the admissions model, and it is not
computationally feasible to include all of them, it is unclear why Prof. Arcidiacono chose to include
specific interactions—for example, allowing the effect of gender and “disadvantaged” status to vary
by race—and not others. Decisions to add interactions to a model like Prof. Arcidiacono’s are
typically guided by a clear economic theory or methodological goal. The typical approach in a model
trying to isolate the effect of Asian-American ethnicity on admissions outcomes would be to include
an interaction between race and disadvantaged status only if the effect of being disadvantaged is
different for Asian-American and White applicants (or, equivalently, if the effect of race is different
for disadvantaged and non-disadvantaged applicants). Prof. Arcidiacono’s results, however, show
that is not the case. In my model, I remove the interactions with race and gender. This is a more
transparent approach that requires fewer subjective judgments about which of the hundreds of
interactions that can be included in such a model should be included.

Arcidiacono’s pooled model. This approach has the additional benefit that it can account for any potential “interaction”
effects associated with specific combinations of the four ratings profiles.
85
Arcidiacono Report, p. 62.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 49

Control variables used in logit models of admission
Professor Arcidiacono’s Models 5 and 6

Additional Variables in Card Models

Race Variables
Race
(White, African-American, Hispanic, Native American,
Hawaiian/Pacific Islander, Asian-American, and Missing)
Base Control Variables
Year, gender, docket
First generation college
Disadvantaged, fee waiver, and financial aid
Dean or Director’s interest list†
Mother and father education level
Early Action†
Athlete†, legacy†, double legacy†
Child of Harvard faculty or staff†

Mother and father occupation
Mother or father deceased
Parent attended Ivy League college
Rural applicant
Intended career
School type (public, private, Archdiocese)
Parent attended Harvard Graduate School
Born in United States, lived outside of United States
Permanent resident of United States
Primary extracurricular activity indicators
Total hours of work

Interactions
Race and intended concentration interacted with gender*
Disadvantaged, early decision†, and legacy† interacted
with race*
Missing data indicators interacted with race*
Profile Ratings
Academic, extracurricular, and athletic ratings
Personal rating (Model 6 Only)

Profile rating combinations

Other Ratings
Alumni interview ratings
Teacher ratings
Guidance counselor rating
Overall rating (Model 6 Only)*

Alumni interview ratings combinations
Teacher ratings combinations
Staff interview ratings

Academic Variables
ACT/SAT Math and Verbal, Average SAT Subject Test Score
Converted GPA and indicator for value of 35
Academic Index Quadratic
High School and Neighborhood Characteristics
High school cluster ID*
Neighborhood cluster ID*

High school characteristics (such as average SAT math)
Neighborhood characteristics (such as median income)
SAT state indicator

Missing Data Indicators
Alumni interviewer rating*, cluster IDs*,
and Average SAT Subject Test Score
Legend:
* Removed from Card Models.
† Included in expanded sample model only.
Source: Augmented Arcidiacono Data

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 50

101. I also correct a variety of technical errors in Prof. Arcidiacono’s sample and control
variables:
Prof. Arcidiacono treats profile ratings of 7, 8, and 9 as low ratings, but
7, 8, and 9 ratings do not appear in the reader guidelines and thus are
more likely erroneous data entries. 86 I treat such entries as missing
ratings.
• Prof. Arcidiacono drops applicants with blank teacher ratings from his
regressions, rather than including them in the “missing” category of his
teacher ratings variables. I include such entries in the “missing”
category.
• Prof. Arcidiacono makes an error when importing the ACT science
scores. I correct this error so that they are imported correctly.
• I remove sample conditions related to the overall rating since the overall
rating is excluded from all of my models and thus there is no need to
exclude applicants with low or missing overall ratings.87
•

5.1.2. A year-by-year model is more appropriate than a pooled model
102. Another important methodological flaw in Prof. Arcidiacono’s approach is his decision
to pool admissions data across years. This decision is flawed for several reasons.
103. First, the admissions process at Harvard is, by its nature, an annual process. Each
applicant is compared to other applicants who applied in that year. A pooled analysis does not reflect
how the process actually works, because it effectively compares applicants from different years to
each other.
104. Second, a closely related problem with a pooled model is that it imposes the assumption
that every factor in the admissions process has the same effect from year to year. Given that the
applicant pool changes from year to year, it is quite possible that the relative abundance and scarcity

2018 Reading Procedures at HARV00015414 – 15.
As noted above, I also exclude the overall rating from all of my models. As discussed above, according to deposition
testimony in this case, race can influence the overall rating. Since my analysis seeks to isolate the incremental effect of
race on admissions decisions, it is inappropriate to include any variables that can themselves be affected by race.
Removing the overall rating from my model is a conservative approach because White applicants have slightly higher
overall ratings, on average, than Asian-American applicants. My analyses show that, even without the inclusion of overall
rating in the models, there is no evidence of bias in admissions decisions.

86
87

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 51

of relevant factors can also change, which can cause the value Harvard places on any given factor to
also change from year to year. Below I provide several examples of how this dynamic might play out.
105. One example is that, during the period for which I have data, Harvard saw a shift in
applicants’ intended concentrations. (See Exhibit 15.)

.88 Because Harvard seeks to admit a
class that is diverse with respect to intended concentrations, the effect of an applicant’s intent to
concentrate in a given field might well change when the aggregate interests of the applicant pool as a
whole vary over time. Thus, for example, an applicant’s intention to concentrate in the humanities
might distinguish an applicant more or less depending on the overall mix of intended concentrations
in the applicant pool in that year.

88

One potential factor contributing to this shift in intended concentrations is that in 2007, Harvard elevated the Division
of Engineering and Applied Sciences to the School of Engineering and Applied Sciences. John A. Paulson School of
Engineering and Applied Sciences, “Timeline,” available at https://www.seas.harvard.edu/about-seas/historyseas/timeline, accessed November 20, 2017.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 52

The mix of intended concentrations for Harvard applicants has changed over time

Source: Augmented Arcidiacono Data
Note: Sample consists of applicants to the classes of 2014 – 2019 in Prof. Arcidiacono’s corrected expanded sample. Applicants with
missing and “Unspecified” intended concentrations are excluded from this chart.

106. Another example is that the definition of the dockets (the geographical divisions Harvard
uses in its admissions process) changed during the time period for which I have data. Starting with
the class of 2015, Harvard introduced the J docket. For the classes of 2015 – 2019, the J docket
included applicants from Arkansas, Kansas, Kentucky, Mississippi, Missouri, Western New York,
Oklahoma, Tennessee, and West Virginia, but for the class of 2014 those applicants were distributed
across other dockets.89 Prof. Arcidiacono’s model cannot account for this change because it estimates
the effect of an applicant’s docket placement on admission only after pooling years together. Thus,
his model estimates docket effects incorrectly because it conflates the two different definitions of

89

See workpaper.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 53

dockets across years.
107. Additionally, as I discuss in greater detail in Section 7 below, Harvard did not employ an
Early Action admissions process for the classes of 2014 and 2015. Starting with the class of 2016, it
reinstated Early Action.90 Prof. Arcidiacono’s model cannot account for these changes because he
pools all the data together into a single model. As a result, the estimated effect of each variable in his
model is calculated using two different admissions regimes—one in which Early Action admissions
existed and one in which it did not. That is problematic for both his expanded and baseline samples.
Excluding Early Action applicants, as he does in his baseline sample, is not sufficient to correct for
the problem, because there is no way to identify which applicants would have applied Early Action
had Early Action existed.
108. Variation in the admission rate across the six admission cycles for applicants with the
same profile ratings combinations provides further justification for estimating the model year-byyear. For example, consider applicants with ratings of 2 on all four dimensions (academic,
extracurricular, personal, and athletic). Applicants with this ratings combination have an admission
rate that varies between 61% and 77% depending on the admissions cycle.91 By pooling data across
year, Prof. Arcidiacono’s model assumes these ratings have the same effect in each year.
109. To formally test whether the effect of various applicant characteristics on applicants’
likelihood of admission is sufficiently similar across years to justify using a “pooled” model as Prof.
Arcidiacono does, I have employed a standard statistical test known as a Wald test (or a chi-squared
test). That test is designed to evaluate the null hypothesis that applicant characteristics have identical
effects on likelihood of admission from year to year. I find that the Wald test rejects that null
hypothesis here, indicating that a pooled model is inappropriate.92 Additionally, as I will discuss in
more detail below, I find that my year-by-year models are better able to predict admission, a further
justification of using year-by-year models rather than a pooled model. Given these results, and the
fundamental fact that Harvard’s admissions decisions are made separately for each year, the
90

Harvard Office of Institutional Research presentation, “Admissions and Financial Aid at Harvard College,” February
2013, HARV00031687 – 1772 (“OIR Presentation”) at HARV00031695.
91
See workpaper.
92
To implement this test, I start with Prof. Arcidiacono’s Model 6 for the expanded, pooled sample, and exclude the
overall rating and interactions with race and gender. I then interact all other control variables with each of his dummy
variables for each year to directly test whether the effects of the control variables change from year to year. In
implementing the test, I combine Native American and Hawaiian/Pacific Islander applicants with Hispanic applicants and
combine personal ratings of 1 and 2 into one dummy variable because there are too few applicants in those categories to
allow me to interact each variable with each separate year dummy. See workpaper.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 54

methodologically sound approach is to estimate a separate model for each year.93
5.1.3. Definition of race
110. In my analysis, I generally use the same method for classifying applicants by race as
Prof. Arcidiacono uses, to ensure comparability of results. However, when estimating a separate
model for each year, I have to combine one race group with another due to the fact that there are very
few applicants of a particular race (e.g., Native American or Hawaiian/Pacific Islander) in any one
year.94 In Prof. Arcidiacono’s model, applicants are classified into mutually exclusive categories of
White, African-American, Hispanic, Native American, Hawaiian/Pacific Islander, Asian-American,
and Missing.95 In my year-by-year models, I use the following mutually exclusive race categories:
(1) White, (2) African-American, (3) Hispanic, Native American, or Hawaiian/Pacific Islander,
(4) Asian-American, and (5) Missing.96 I combine Native American and Hawaiian/Pacific Islander
applicants with Hispanic applicants because the increased probability of admission associated with
Native American and Hawaiian/Pacific Islander ethnicity is most similar to the increased probability
of admission associated with Hispanic ethnicity.97 To ensure that my estimate of the alleged bias
against Asian-American applicants is robust to this change, I have tested whether using this adjusted
definition of race has any substantive effect on the Asian-American coefficient within Prof.
Arcidiacono’s pooled sample. It does not.98
111. I have also considered the possibility (raised by Prof. Arcidiacono) that the fact that
some applicants to Harvard are not classified as belonging to any racial group (i.e. are “Missing”
race) might lead to an underestimate of the alleged bias against Asian-American applicants. For the
purpose of this analysis, I use other variables in the Harvard data with information about an
applicant’s race that Prof. Arcidiacono did not use in creating his definition of race. For example, if
93

Prof. Arcidiacono himself finds evidence that it is better to estimate the model separately for each year. For example, he
presents a model in his report using the expanded sample in which he interacts year and race (thus allowing each race to
have a separate effect in each year). That model finds that the effect of race differs in a statistically significant fashion
across years (Arcidiacono Report, Appendix B, Table B.8.1).
94
Prof. Arcidiacono also combines smaller race groups when he estimates a model that interacts his race categories with
year (Arcidiacono Report, p. 69, footnote 69).
95
This race definition is a variable available in the Harvard database. In 2010, Harvard began using an additional
methodology that allows applicants who self-identified with more than one race to be counted in more than one category
(Deposition of Elizabeth Yong, March 24, 2017 (“Yong Deposition”), pp. 134–137).
96
I also combine Hawaiian/Pacific Islander applicants with Asian-American applicants, rather than grouping them with
Hispanic applicants, in a sensitivity of my preferred model (discussed in more detail below).
97
See workpaper.
98
See workpaper.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 55

an applicant reports her race to the College Board when taking the SAT, it is provided to Harvard
along with the test score. Using these variables, it is possible to identify the race of many applicants
that are classified as missing race in Prof. Arcidiacono’s analysis. In fact, I am able to classify nearly
70% of the 10,000 applicants classified as having a “missing” race.99 When I re-estimate Prof.
Arcidiacono’s model with these additional applicants’ race information filled in, I find that in fact his
estimates of the effect of Asian-American ethnicity become slightly less negative, not more.100 That
directly contradicts Prof. Arcidiacono’s claim that the exclusion of these applicants’ races likely
causes his model to underestimate the bias against Asian-American applicants.
5.1.4. Prof. Arcidiacono’s Models 1-4 are not reliable
112. Prof. Arcidiacono offers six different models to estimate the effect of Asian-American
ethnicity on the probability of admission. My analysis builds exclusively on Prof. Arcidiacono’s
Models 5 and 6, for two reasons.
113. First, Prof. Arcidiacono states that “Model 5 is the most useful of [his] models for
determining the effect/impact of race in admissions decisions,”101 and Mr. Kahlenberg uses Model 6
as his preferred model for simulating race-neutral admissions practices. SFFA’s own experts thus
agree that Models 5 and 6 are the most reliable.
114. Second, as explained above, Models 1, 2, 3, and 4 are unreliable because they do not
account for any of Harvard’s ratings on non-academic dimensions. As detailed in Section 3 above,
Harvard’s admissions process considers a wide variety of non-academic factors, and non-academic
excellence is rarer in the Harvard applicant pool than academic excellence. Harvard’s profile and
school-support ratings play an essential role in capturing non-academic information, much of which
is not otherwise quantified. Because Prof. Arcidiacono’s Models 1–4 ignore that critical information,
they cannot reliably estimate the effect of race.
115. Exhibit 16 helps illustrate this point. It reports the Pseudo R-Squared value for each of
Prof. Arcidiacono’s Models 1–6. The Pseudo R-Squared statistic provides a useful summary measure
of the extent to which the variables included in a model explain the outcome being modeled (in this
case, admission to Harvard). It can take on values ranging from zero to one; the closer it is to one, the
more the model explains about Harvard’s admission decisions. Models 1–4 have Pseudo R-Squared
99

See workpaper. Although I understand that admissions officers rely on applicants’ self-identification of their race on
the application (see Deposition of Grace Cheng, April 7, 2017, pp. 114–115; Banks Deposition, p. 190; Fitzsimmons
Deposition, pp. 239–240), I use race information reported by the applicant on the SAT, SAT II, and ACT tests for the
limited purpose of this sensitivity analysis. I do not include this additional race information in the rest of my models.
100
See workpaper.
101
Arcidiacono Report, p. 62.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 56

values of 0.34 or lower—very low, and much lower than the Pseudo R-Squared values of Models 5
and 6, which jump to 0.57 and 0.65, respectively (for the expanded sample). That is because Models
1–4 ignore critical information on which Harvard relies when making admission decisions.

Explanatory power of Professor Arcidiacono’s logit models of admission

Source: Arcidiacono Report, Appendix B, Tables B.7.1 and B.7.2.

5.1.5. The expanded sample is more appropriate than the baseline sample
116. Prof. Arcidiacono presents his models using two different samples—one that he refers to
as the “baseline sample” and one that he refers to as the “expanded sample.” The baseline sample
removes lineage applicants, recruited athletes, children of Harvard faculty and staff, candidates who
appear on the Dean’s or Director’s interest lists, and Early Action applicants. My analyses rely on the
expanded sample, for several reasons.
117. First, as a general matter, Harvard compares all of its applicants in each year to all other
applicants in the pool for that year; it does not conduct separate admissions processes for discrete
subsets of the pool. Harvard seeks a diverse class in each year on any number of dimensions—
academic, extracurricular, geographic, racial and ethnic, and so on. Thus, the fact that some
candidates with particular attributes (such as lineage applicants or recruited athletes) have a higher
likelihood of admission does not mean that they should be completely excluded from the analysis.
Such candidates are still compared to other candidates on all dimensions, and their candidacy can
affect how other decisions are made. By throwing such information out of the analysis, the model
cannot use that information to explain why other applicants were or were not admitted.
118. This methodological flaw is particularly a concern for Prof. Arcidiacono’s decision to
remove from his baseline sample applicants for Early Action admission. This decision is inconsistent
with how Harvard’s admissions process works. It is my understanding that Harvard does not have a
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 57

different standard for admission in the Early Action process, and most applicants who apply early and
are not admitted have their applications “deferred” to the Regular Decision phase,102 where they
compete with applicants who did not apply early.103 Removing early applicants from the sample thus
has the effect of modeling only part of the regular admissions cycle, excluding many applicants with
whom the included applicants are competing for spots.
119. Second, as noted above, the Early Action process did not exist in two of the years of data
used in Prof. Arcidiacono’s model. Thus, for years in which there was no Early Action process (the
class of 2014 and 2015 admissions cycles), Prof Arcidiacono’s “baseline sample” includes a different
set of applicants than in years for which Early Action was available. Further, because Prof.
Arcidiacono pools data across all years and then excludes Early Action applicants for years in which
Early Action existed, his baseline sample combines multiple years of data that have different
definitions of a “baseline” sample, creating a pooled sample that is inconsistent. That is a major
problem with his “baseline” sample and pooled model.
120. Finally, because it is important to estimate the models separately by year, limiting the
sample to Prof. Arcidiacono’s “baseline” sample unnecessarily reduces the sample size of the yearby-year models, which reduces the power and precision of the models.
5.1.6. The importance of factors that Harvard values but that are not measured in the data
121. As detailed throughout this report, Harvard’s admissions process considers nonacademic factors that are relatively scarce in the applicant pool and difficult to quantify in a
regression model. Even after enriching my admissions model to capture a variety of such factors that
are missing from Prof. Arcidiacono’s model (and to improve its predictive power relative to Prof.
Arcidiacono’s model), my model still does not perfectly explain all of Harvard’s admissions
decisions. This implies that there are additional factors not measured by my model that are important

102

See workpaper.
McGrath Deposition 2015, p. 210 (“Q. And then everything we just said about the information that gets presented to
the subcommittee is the same for regular action as it is for early action? A. Yes.”); Weaver Deposition, Volume II, pp.
172–173 (“Q. Besides the timing, what other variations are there between …early action and regular action? … A. There
are differences between the two in the sense of timeline and the quantity of applications; however, the process and the
way in which a folder moves through the process is similar.”); Ray Deposition, p. 55 (“Q. When you go to subcommittee
in the regular action review process, …do you follow the same format that you did in early action review? … A. Yes. …
Q. And do you typically give the same designations for students—namely, admitted, wait list, rejected, FAO hold—
during the subcommittee process and regular action review? ... A. Yes. The only different action is that there is no defer
action in regular action.”).
103

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 58

to Harvard’s admissions decisions. The omission of such factors from the model presents a classic
example of a problem known as “omitted variable bias,” or what I have referred to above as the
“missing data” problem.104
122. Omitted variable bias occurs whenever a regression model omits variables that (1) are
correlated with the variable of interest and (2) affect the outcome variable. In that circumstance, the
effect of the omitted variable on the outcome may incorrectly be attributed to the variable of interest.
Here, the variable of interest is race, so the omission of variables that are correlated with race and
affect admissions outcomes—such as the non-academic factors discussed throughout this report—can
lead the model to misattribute to race differences in admissions outcomes that are in fact attributable
to the omitted variables.
123. Statistical methods can help quantify the importance of unmeasured, individualized
factors in the decision process relative to factors that are more easily measured. These methods can
help us understand the degree to which factors outside of the model might bias the results, and/or
explain the reasons a specific applicant was ultimately admitted or denied admission. Below are four
widely accepted methods that I will use in the remainder of this section, and that will be important in
showing that Prof. Arcidiacono’s model is missing critical information.
• Measures of overall fit and predictive accuracy: These statistics measure
how well the model explains, or predicts, the outcome of interest (in this
case, admission to Harvard). I will rely primarily on two such metrics.
The first is Pseudo R-Squared, a measure of how well the variables
included in the model explain the outcome. The second is the fraction of
admitted applicants for whom the model correctly predicts the actual
admission outcome.105
• Predicted probability of admission for each individual applicant:
Whereas the metrics discussed above reflect how well the model
104

James H. Stock and Mark W. Watson, Introduction to Econometrics (Pearson, 2015), pp. 183–184 (“If the regressor is
correlated with a variable that has been omitted from the analysis and that determines, in part, the dependent variable,
then the OLS estimator will have omitted variable bias.”); Sharmila Choudhury, “Reassessing the Male-Female Wage
Differential: A Fixed Effects Approach,” Southern Economic Journal 60(2), 1993, pp. 327–340 at p. 327 (“The
conventional approach of economists has been to estimate earnings as a function of various socio-economic
characteristics. The observed wage gap is decomposed into a part explained by productivity related factors and an
unexplained residual, traditionally labelled as discrimination. While it is possible that the unexplained variation earnings
is the result of discrimination, it is also possibly the result of model misspecification ... we address the misspecification
that could possibility arise from omitted variables…”).

105

Because the logit model estimates a probability of admission for each applicant, I compute this statistic by ranking
applicants from highest to lowest predicted probability of admission and considering the top-ranked applicants to be
admitted, such that the number of predicted admitted students matches the number of actual admitted students.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 59

explains admissions outcomes in aggregate, the importance of
unmeasured factors in any individual admissions decision can be
quantified using the estimated probability of admission for each
individual applicant. For example, if the model generates an estimated
probability of admission close to zero for an applicant who is actually
admitted, or vice versa, it suggests that there are unobserved factors that
substantially affected the admission outcome. A particularly useful
exercise is to compare the predicted probability of admission for any
given applicant to the final admission decision. The difference between
the predicted probability of admission and the actual admission decision
is a measure of the importance of unobserved factors that are valued by
admissions officers but not included in the model. The larger the
difference, the more important unobserved factors were in the final
decision.
• Sensitivity of coefficients to inclusion/exclusion of additional control
variables: Another way to assess the influence of unmeasured factors on
a given outcome variable is to estimate “sensitivity” analyses that
include different sets of control variables, testing how the effect of a
particular variable of interest changes when different sets of controls are
included. Prof. Arcidiacono employs this analysis himself when using
his Models 1–6, as will I in order to better understand the effect of
factors that cannot be included in my estimation.
• Subgroup analysis: A closely related method for assessing the
importance of unmeasured factors is subgroup analysis. If racial bias is
the cause of a disparity between racial groups in an outcome like
admission to Harvard, then one would expect to see the disparity persist
across all relevant subgroups, time periods, and outcomes in the data.
For example, a bias against applicants of a particular race should affect
men and women of that race alike, and should affect members of that
race across all years, since race is consistent across gender and time. On
the other hand, if the racial disparity is caused by unobserved factors
rather than by bias, it is much more likely that the disparity will vary
across subgroups because, simply by chance, the relative strength and
weakness of each racial group on unmeasured factors will differ by
subgroup. Similar logic applies if the disparity at issue is not consistent
across different outcome measures––if the admissions process is in fact

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 60

biased, there should be consistent evidence of bias not just in ultimate
admissions decisions but in other types of outcomes that reflect the
judgment of admissions officers, such as profile ratings. I employ these
types of analyses in Section 5.3 below.
5.1.7. The importance of average marginal effects
124. One final technical note warrants discussion. In the appendix tables of his report, Prof.
Arcidiacono reports only the logit coefficients of the race variables from his regression models.
Those coefficients show the marginal effect of a given variable (e.g., an indicator for Asian-American
ethnicity) on the logarithm of the odds (the so-called log-odds) of admission, rather than the marginal
effect of a given variable on the probability of admission for a given candidate. Importantly, in a logit
model, the marginal effect of any given variable on an applicant’s probability of admission varies
depending on that applicant’s other characteristics, and there is no single parameter that measures the
gap in admission probabilities between different subgroups. As a result, simply reporting the logit
coefficient for a given variable does not convey the effect of that variable across all applicants in the
relevant population (here, Asian-American applicants).
125. For example, consider an applicant to Harvard who has an academic rating of 4 or worse.
She will have very little chance of admission given Harvard’s high academic standards. Thus, even if
she was very active in her high school, served as president of the student government, and
volunteered at numerous community organizations (all characteristics Harvard values), she would
still have very little chance of admission. Those factors will have essentially zero marginal effect on
her probability of admission. On the other hand, if the same candidate had an academic rating of 1 or
2, then the marginal effect of her strong extracurricular and community service record on her
probability of admission would be much larger. This is what is referred to as a “non-linear” effect—
the effect of the student’s non-academic achievements depends on whether her academic
qualifications are strong enough for her to be in the running.
126. Because of these non-linear effects, the typical way to summarize the marginal effect of
a variable in a logit regression is to report its average marginal effect across all individuals who
possess the trait in question—rather than simply reporting its logit coefficient, as Prof. Arcidiacono
does.106 For example, in the hypothetical above, one would report the average marginal effect of a
106

A. Colin Cameron and Pravin K. Trivedi, Microeconometrics: Methods and Applications (Cambridge University
Press, 2009), pp. 467, 501 (“[T]here are several ways to compute an average marginal effect. It is best to use …the
sample average of the marginal effects…Typically these [marginal effects] are then averaged over individuals to give an
average marginal effect[.]”); William H. Greene, Econometric Analysis (Pearson, 2008), p. 775 (“For computing marginal
effects, one can evaluate the expressions at the sample means of the data or evaluate the marginal effects at every
observation and use the sample average of the individual marginal effects…Current practice favors averaging the
individual marginal effects when it is possible to do so.”).
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 61

candidate’s extracurricular achievements across all candidates. If the average marginal effect of a
given variable is not statistically different from zero, one can conclude that on average the variable
does not have a significant effect on the outcome of interest. For this reason, I report all effects of
race in my report as average marginal effects.
127. Another shortcoming of Prof. Arcidiacono’s approach to reporting the logit coefficients
is that, because he estimates the effect of Asian-American ethnicity separately for men and women
and for those who are and are not identified by Harvard’s admissions officers as disadvantaged, most
of his analysis does not quantify the overall effect of ethnicity for the full set of Asian-American
applicants.107 The Asian-American logit coefficient (-0.367) that he reports in his appendix table
B.7.1 and discusses in Section 3.7 of his report actually refers to the effect of Asian-American
ethnicity only for male applicants who are not disadvantaged, not the effect for the general
population of Asian-American applicants, including those who are disadvantaged and those who are
female.108 To calculate the effect on the log-odds of admission of Asian-American ethnicity for nondisadvantaged female applicants, for example, one must add together the Asian-American coefficient
and the Asian-American*female coefficient, yielding an effect on the log-odds of only -0.089—less
than one-quarter the size of the effect that Arcidiacono misleadingly reports.109 Calculating an
average marginal effect, as I do throughout this report, corrects this problem by reporting a single,
average estimated effect of Asian-American ethnicity on likelihood of admission across all AsianAmerican applicants.
5.2. My enriched model finds no statistically significant evidence of bias
128. I now turn to the results of my statistical model. As detailed above, I start with Prof.
Arcidiacono’s model and then include a richer set of control variables that he does not include in his
model and that more fully account for the substantial consideration Harvard gives to non-academic
factors, including contextual factors like high school, neighborhood, and family background. I then
use the model to test whether the disparity between Asian-American and White admission rates can
be explained by factors in the model other than race. As I show below, once additional relevant
factors are included in the model, Asian-American ethnicity has no consistent statistically significant

107

Prof. Arcidiacono’s Table 7.2 is an exception.
This also applies to Prof. Arcidiacono’s Table B.7.2 and various other tables reporting logit coefficients in his report,
such as those for his ratings regressions. Additionally, he includes interactions between race and missing variable
indicators for variables such as SAT II average, alumni interview rating and College Board cluster identifiers, so the
coefficients he reports are actually for Asian-American non-disadvantaged male applicants who are not missing these
covariates.
109
-.089 = -.367+.278 summing logit coefficients on Asian-American and female*Asian-American from Prof.
Arcidiacono’s Table B.7.1.
108

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 62

negative effect on applicants’ likelihood of admission.
5.2.1. With better and more complete control variables included in Prof. Arcidiacono’s regression model,
there is no statistically significant gap in admission rates between Asian-American and White applicants
129. Exhibit 17 presents one of the key findings of my analysis: The alleged effect of AsianAmerican ethnicity on applicants’ likelihood of admission is statistically insignificant even in a
model that pools all applicants across years as Prof. Arcidiacono does.
130. Each row in Exhibit 17 reports the average marginal effect of Asian-American (relative
to White) ethnicity for a particular specification of Prof. Arcidiacono’s Model 6, including the
additional changes I make to Model 6 described above. The average marginal effect is the average
change in the estimated probability of admission associated with being Asian-American as opposed
to White, calculated across all Asian-American applicants in the sample.

Pooled logit models of admission do not show evidence of bias against Asian-American applicants

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Table shows the average marginal effect of race on admission for Asian-American applicants. The Card pooled model uses Professor
Arcidiacono’s corrected expanded sample; all other models use Professor Acidiacono’s expanded sample. Marginal efects are calculated
relative to White applicants (using the same definition of race as Professor Arcidiacono). * indicates significance at the 5% level. Marginal
effects are reported as percentage point values.

131. The first row is calculated directly from Model 6 in Prof. Arcidiacono’s report. It shows
that the average marginal effect of Asian-American ethnicity in Model 6 is -0.46. This means that,
relative to the average White applicant, the average Asian-American applicant has a lower probability
of admission to Harvard—by 0.46 percentage points—controlling for all of the variables in Prof.
Arcidiacono’s model. This effect is statistically significant.
132. The second row also relies on Prof. Arcidiacono’s Model 6, but removes the overall
rating, which should not be included in any model that is attempting to estimate the effect of race

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 63

because (as discussed above) the overall rating may be influenced by an applicant’s race. In this
specification, the average marginal effect of Asian-American ethnicity becomes more negative, at 0.58, and remains statistically significant. The third row then removes Prof. Arcidiacono’s
interactions of race with other variables (such as disadvantaged status, gender, and missing variable
indicators), and also removes interactions of gender with other variables (such as intended
concentration), which for the reasons discussed above should not be included. In this specification,
the average marginal effect of being Asian-American changes only slightly to -0.53 and remains
statistically significant. Moving forward, when I refer to Prof. Arcidiacono’s model, I will refer to the
version in row 3, as that is the version that I will build on as I enrich the model.
133. The fourth row of Exhibit 17 reports the key results of my enriched model, where I begin
with Prof. Arcidiacono’s Model in row 3 and add in additional control variables detailed in Exhibit
14 above, including better measures of high school quality, high school and neighborhood
demographics, socioeconomic status, and staff interview ratings.110
134. When I include these additional variables, the average estimated marginal effect of
Asian-American ethnicity falls by over 70% to -0.14, and—crucially—it becomes statistically
insignificant at the conventional 5% significance level. In other words, the model finds that there is
no statistically meaningful effect of Asian-American ethnicity on applicants’ likelihood of admission,
controlling for all of the variables in my enriched model.111
135. Exhibit 18 shows in more detail how the average marginal effect of Asian-American
ethnicity falls as I add additional controls to the pooled model. The addition of information on
parental occupations causes the average marginal effect to fall from -0.59 to -0.41. Adding detailed
high school and neighborhood information (on top of the parental occupation information) causes the
effect to fall further to -0.29.112 Further expanding the set of controls to include all the additional
controls I use in my model (e.g. intended career, staff interview ratings, and an indicator of whether
the applicant was born in the United States) causes the effect to fall still further, to -0.14, and to
become insignificant. At each step, I test whether the variables I have added are jointly statistically
110

Some variables in my model (but not Prof. Arcidiacono’s model), such as the detailed College Board high school and
neighborhood characteristics and the rural indicator, are unavailable for some applicants (primarily those on international
dockets or those who are home-schooled). Thus, when I add these variables to the model, applicants missing this
information are no longer included in the regression sample. Such applicants account for only 4.85% of the sample (see
workpaper). Prof. Arcidiacono includes such applicants in his sample by assigning them all to the same high school and
neighborhood clusters. This inappropriately groups applicants from varied backgrounds (such as those who are homeschooled in the United States and those attending international high schools) into the same cluster identifier.
111
Treating Hawaiian/Pacific Islander applicants as Asian-American applicants attenuates the estimated effect even
further to -0.09 (See workpaper).
112
I reviewed the 60 individual high school and neighborhood variables available in the College Board data and found
several were redundant (with one another or with information available in the Harvard database) or had other limitations
that warranted their not being included.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 64

significant, and in each case they are.

Additional control variables attenuate the estimated effect of Asian-American ethnicity

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Data are from applicants in Professor Arcidiacono’s corrected expanded sample. Marginal effects are calculated relative to White
applicants. * indicates significance at the 5% level. Marginal effects are reported as percentage point values. Other variables include
intended career, school type, parent attended Ivy League college, parent attended Harvard graduate school, parent living or deceased
status, rural indicator, permanent resident indicator, and staff interview rating.

136. As detailed above in Sections 3 and 4, a fundamental problem with Prof. Arcidiacono’s
models is that they put a great deal of weight on academic variables by including both the academic
rating and the various quantitative academic measures that inform that rating, but they include less
information on the critical non-academic factors (including contextual factors like high school,
neighborhood, and family background) that Harvard considers, and that differ on average between
White and Asian-American applicants. As shown in the prior two exhibits, when I address this
concern and include more variables that can capture differences across candidates in life experience
and circumstance, the disparity between Asian-American and White admission rates is fully
explained by the set of control variables in the model.
137. This result should not be surprising because a similar pattern is present (albeit to a lesser
degree) in Prof. Arcidiacono’s own models. Specifically, as he adds non-academic variables to his
model, including measures of socioeconomic status and non-academic ratings, the alleged negative
effect of Asian-American ethnicity is attenuated.113 My enriched model has the same feature; it
simply adds a more inclusive set of measures of such factors into the model.
138. This is still a pooled model, as opposed to the year-by-year models that I consider
methodologically superior and that I discuss below. In other words, even if I accept Prof.
Arcidiacono’s methodological choice to use a pooled model, the addition of proper control variables

113

Arcidiacono Report, Appendix B, Table B.7.2.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 65

to his model negates any statistically significant negative effect of Asian-American ethnicity.
139. Before moving on, I want to respond to one additional argument Prof. Arcidiacono
makes that is related to this point. Prof. Arcidiacono points to documents produced in this litigation
from Harvard’s Office of Institutional Research (OIR), summarizing statistical analyses performed by
that office, as supposedly corroborating his findings and his methodology. A careful review of the
relevant analyses, however, indicates that OIR’s research methodology actually supports my
methodological approach over Prof. Arcidiacono’s. Specifically, the documents indicate that OIR
understood that its models were “basic” and “preliminary” and that, like Prof. Arcidiacono’s, they
were missing important factors in the admissions process—particularly non-academic factors. For
example, one of the documents states that “[t]here are a variety of factors that quantitative data is
likely to miss or ratings not capture,” and then lists as examples “[e]xceptional talent,” “[t]he role of
context cases,” “[t]he role of the personal statement/essay,” and “[m]easures of socioeconomic
status.”114 In other words, OIR’s documents recognize the same limitations in its analysis that I
recognize in Prof. Arcidiacono’s, and thus provide further support for my approach of expanding the
set of control variables to help the model better control for the many non-academic factors that are
important to the admissions process.
5.2.2. When the model is estimated year-by-year, it finds no evidence of a statistically significant negative
effect of Asian-American ethnicity
140. As detailed in Section 5.1.2, in my opinion the correct way to model admissions
decisions at Harvard is to examine each year separately. Prof. Arcidiacono’s model does not do that;
instead, it imposes the unrealistic assumption that Harvard’s admissions process compares applicants
across years and that each factor has the same effect in every year. The reality of the admissions
process is quite different. Candidates compete only against the other candidates applying in that year,
and Harvard’s admissions decisions in each year depend on the specific set of applicants in the pool
that year.115 Moreover, as noted earlier, certain factors (like the use of Early Action) change from
year to year.
141. In Exhibit 19, I report results for the year-by-year models, my preferred methodology.
What I find is generally consistent with the pooled model. The average marginal effect of Asian114

OIR Presentation at HARV00031722.
A student who is admitted in a prior year but chooses to defer his admission, or a student offered deferred admission in
a prior year, is considered part of the admitted class in the year for which he will enroll but is still compared, in the
admissions process, only against other applicants in the year when he originally applied.

115

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 66

American ethnicity on applicants’ likelihood of admission across all six years of data116 is statistically
indistinguishable from zero (-0.02), just like the average marginal effect in the pooled model,
indicating no statistical evidence of bias.117
142. However, by estimating the model year-by-year, I also gain some important information.
Specifically, in four of the six years the coefficients on Asian-American ethnicity are actually small
and positive—in other words, Asian-American ethnicity (relative to White ethnicity) is associated
with a higher likelihood of admission in those years, controlling for all other factors. The years with
positive estimated effects include three of the four years since the reinstatement of Early Action with
the class of 2016 cycle.118

116

My pooled model generates a single estimate of the average marginal effect of Asian-American ethnicity on
applicants’ likelihood of admission. By contrast, my year-by-year model generates six different estimates—one for each
class. To ensure that my year-by-year estimates are comparable with Prof. Arcidiacono’s pooled estimate, I average the
six year-by-year estimates to obtain an average effect across all six years of data. This approach allows me to use all the
available years of data but estimate models that more accurately reflect Harvard’s admissions process.
117
This result also holds if I include average Advanced Placement exam scores in the 2017 – 2019 models (the only years
for which they are available in the data). Prof. Arcidiacono excludes these from his pooled model analysis because they
were only available in later years, but he argues that excluding such measures likely causes him to underestimate bias
since these are measures on which Asian-American applicants are relatively strong (Arcidiacono Report, pp. 77–78). His
dataset contains a variable for average AP exam scores for the classes of 2018 and 2019. I increase the coverage of this
variable to include 2017 AP scores (which are stored in a different field) and include the expanded variable in my yearby-year models for 2017, 2018, and 2019. See workpaper.
118
If I estimate this model treating Hawaiian/Pacific Islander applicants as Asian, the estimated effect becomes positive
(though still statistically insignificant) on average across the six years. See workpaper.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 67

Year-by-year logit models of admission show no consistent or statistically significant evidence of
bias against Asian-American applicants

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Table shows the average marginal effect of race on admission for Asian-American applicants relative to White applicants using
Professor Arcidiacono’s corrected expanded sample. * indicates significance at the 5% level. Marginal effects are reported as percentage
point values.

143. The predictive accuracy of my year-by-year enriched model is higher than that of all of
Prof. Arcidiacono’s models. As shown in Exhibit 20, my preferred model with the additional
information correctly predicts the admissions outcome for 74% of applicants, while Prof.
Arcidiacono’s preferred model (Model 5) correctly predicts the outcome for only 67% of applicants.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 68

Card model has higher predictive accuracy than Prof. Arcidiacono’s preferred model

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Table shows the total share of admitted students correctly predicted by the model. Card models use Professor Arcidiacono’s
corrected expanded sample; all other models use Professor Acidiacono’s expanded sample. Predictions assume that the applicants are
admitted in order of their predicted probability of admission from the model.

144. My model includes all applicants, including those who are waitlisted and then admitted
or denied admission from the waitlist. Prof. Arcidiacono presents an analysis comparing the share of
applicants of each race who were waitlisted and then denied admission to the admission rate of all
applicants of each race. He suggests that the fact that Asian-American applicants are more likely to
be denied admission after having been waitlisted, while having the lowest overall admission rate,
reflects bias against Asian Americans.119 That analysis is fundamentally incomplete and misleading,
and cannot be taken as evidence of bias, because it does not account for the many qualifications that
differ on average between Asian-American and White applicants. My admission model discussed
above, which includes all applicants (including those who were waitlisted) and does account for
differences in qualifications, finds no evidence of bias against Asian-American applicants.
5.2.3. Prof. Arcidiacono’s analysis does not support the conclusion that the personal rating is biased
145. The models discussed above include as a control variable Harvard’s personal rating.
Using an ordered logit model that predicts personal ratings, Prof. Arcidiacono has argued that the
personal rating is biased against Asian-American applicants. Based on this result, he then argues that
the inclusion of the personal rating in the model is inappropriate. As discussed in Section 2 above,
there are several reasons why Prof. Arcidiacono’s statistical evidence of bias in the personal rating is
weak and does not justify the exclusion of the personal rating from his model. Here, I expand on this
issue.
146. First, Prof. Arcidiacono’s model of personal ratings cannot reliably explain the
119

Arcidiacono Report, pp. 31–32.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 69

assignment of personal ratings. The Pseudo R-Squared value of the model is 0.28, which is quite low;
for example, Prof. Arcidiacono’s more reliable model of the academic rating has a Pseudo R-Squared
value of 0.56.120 Additionally the model has very low predictive accuracy. Of the 47 applicants in
Prof. Arcidiacono’s sample who have personal ratings of 1, his model correctly predicts their rating
zero percent of the time, and of the 30,976 applicants with a rating of 2, it correctly predicts their
rating only 45% of the time.121
147. As detailed above, a common methodological challenge in assessing the potential for
racial bias using regression models is that a model almost always excludes some relevant
information. This concern is particularly significant in attempting to model Harvard’s personal rating,
which considers many individualized and hard-to-quantify factors (i.e., the “missing data” I discuss
above). Thus, if a regression estimates that race affects applicants’ personal ratings, there is a serious
question whether that estimated effect might actually be explained not by race but by racial
differences in some factor that is not included in the model and that affects the personal rating—in
other words, by omitted-variable bias (or “missing data”). One clear example of such missing data is
an applicant’s personal essay, which according to documents and testimony in this case is an
important consideration in the determination of the personal rating.122
148. As discussed above, one way to determine if the missing data problem is affecting the
estimated effects of race in a particular model is to consider how the estimated effect in the model
changes as more of the available variables are added to the model. Importantly, Prof. Arcidiacono’s
own regression results show that the estimated effect of Asian-American ethnicity on the personal
rating shrinks as non-academic factors are added to his model of the personal rating. This pattern
suggests that, were more information available, the alleged effect could shrink further. For example,
in Table B.6.7 of Prof. Arcidiacono’s report, the coefficient of Asian-American ethnicity is -0.542 in
Model 3 before he has added controls for neighborhood and school background and for the relevant
ratings that feed into the personal rating. When he adds those controls (in his Model 5), the
coefficient falls to -0.366.123 If the model could account for unobserved factors like the personal
120

Arcidiacono Report, Appendix B, Table B.6.5 and Table B.6.7.
See workpaper.
122
See, for example, Banks Deposition, pp. 79–80 (“Q. And for each of those categories, can you tell me how they were
assigned a numerical score?...[A] Extracurricularly, quality of achievement, strength of performance in any particular
domain, personal qualities, some grasp of the candidate’s personality, interest in other people, cooperation with others, a
sense of responsibility as gleaned from teacher recommendations, personal interview, personal essay, et cetera. Q. Okay.
So for the last category, the—the main inputs you would look at were recommendations, interview, and anything else? A.
The candidate’s essay.”); Walsh Deposition, p. 60 (“Q. How would you calculate that score?…[A.] I would like to take
into consideration whatever relevant information I had were that his essay, her essay, her interview, and the opinions
about that applicant as expressed by others.”); Ray Deposition, pp. 21–22 (“Q. What are the materials that you use—
materials or considerations that go into determining this person’s score?…A. For example, content in recommendation
letters, personal essays.”).
123
Arcidiacono Report, Appendix B, Table B.6.7.
121

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 70

essay, the gap could fall further.
149. Another sign that Prof. Arcidiacono’s regression models of the personal and overall
ratings are not capturing actual bias against Asian-American applicants is that his models find a
statistically significant positive effect of Asian-American ethnicity on the academic and
extracurricular ratings. As noted above in Section 5.1.6, such a pattern calls into question whether the
effects his models attribute to race are more properly explained by factors that are missing from his
models (either because he does not include them or because they are unobservable). If Harvard were
in fact biased against Asian-American applicants, it would make little sense for Harvard to give an
unexplained advantage to Asian-American applicants in the academic and extracurricular ratings. On
the other hand, if Harvard were not biased, but the ratings models were simply missing relevant
variables that explain the differences across race in ratings assignments, it would not be surprising to
see an inconsistent pattern of “bias” across the profile ratings.
150. Further, as detailed in Section 3, the essential function of the ratings is to quantify the
otherwise unobservable information about applicants that admissions officers discern from their
intensive review of each file. It is therefore unsurprising that regression models struggle to reliably
explain the ratings; the whole point of the ratings is to capture information that is hard to measure.
151. Despite my view that Prof. Arcidiacono’s analysis does not support an inference that the
personal rating is biased against Asian-American applicants, I have also conducted an analysis that
assumes for the sake of argument that the personal rating is biased, and therefore removes it from the
model. This approach is an extremely conservative analysis that overcorrects for any concern of bias
in the personal rating, because it completely removes from the model the personal rating (a factor on
which White applicants, in aggregate, are relatively stronger than Asian-American applicants), rather
than removing only the allegedly discriminatory component of the rating. In fact, Prof. Arcidiacono’s
Table 6.1––which uses his personal ratings regression to calculate the share of Asian-American
applicants who would receive a rating of 1 or 2 under the assumption that there was no bias in the
personal rating––shows that White applicants are still, on average, a bit more likely than AsianAmerican applicants to have a personal rating of 1 or 2.124
152. As Exhibit 21 shows, even in this very conservative model that ignores an important
dimension of the admissions process on which White applicants are relatively strong, I still find only
weak and inconsistent evidence of a disparity between Asian-American and White admission rates.
Specifically, I find no evidence of a significant negative effect of Asian-American ethnicity in five of
the six years of data I analyze.

124

Arcidiacono Report, p. 57.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 71

Logit model of admissions removing personal rating

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Table shows the average marginal effect of race on admission for Asian-American applicants relative to White applicants using Prof.
Arcidiacono’s corrected expanded sample. * indicates significance at the 5% level. Marginal effects are reported as percentage point values.

153. Additionally, Exhibit 22 shows the average marginal effect of Asian-American ethnicity
if I remove the only class for which there is a statistically significant negative effect (the class of
2018) from my sensitivity analysis that excludes the personal rating. When I focus my analysis on the
five admissions cycles other than 2018, the estimated effect of Asian-American ethnicity in each of
those five years is statistically insignificant and the overall, average estimated effect across all five
years becomes statistically insignificant (falling by 21% relative to the estimated effect over all six
years). In other words, even if I exclude the personal rating from the model, there is no statistically
significant gap in admissions between Asian-American applicants and White applicants outside of the
2018 admissions cycle.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 72

Excluding 2018, logit model of admissions without personal rating shows no evidence of bias
against Asian-American applicants

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Table shows the average marginal effect of race on admission for Asian-American applicants relative to White applicants using Prof.
Arcidiacono’s corrected expanded sample. * indicates significance at the 5% level. Marginal effects are reported as percentage point values.

154. Before moving on, I want to respond to three other arguments offered by Prof.
Arcidiacono in support of his claim that the personal and overall ratings are biased. First, Prof.
Arcidiacono’s model of the overall rating, like his model of the personal rating and other nonacademic ratings, is weak; it has a Pseudo R-Squared value of just 0.34.125 Given the evidence
detailed above that the estimated negative effect of Asian-American ethnicity on applicants’
probability of admission shrinks as available non-academic qualifications are added to the model, and
given that non-academic qualifications are harder to measure than academic qualifications, the small
negative effect that the model attributes to Asian-American ethnicity is not reliable evidence of bias;
it is entirely possible and even likely that that effect is attributable to omitted non-academic variables.
Additionally, Prof. Arcidiacono’s overall rating model has very poor predictive accuracy. Of the 109
applicants in Prof. Arcidiacono’s sample who have overall ratings of 1 (including pluses and
minuses), his model correctly predicts their rating only 18% of the time, and of the 8,124 applicants
with a rating of 2 (including pluses and minuses), it correctly predicts their rating only 28% of the
time.126 Further, as explained above, I have not included the overall rating in any of my regressions
because it is the one rating that may be influenced by applicants’ race (in the sense that, for example,
the overall ratings of African-American, Hispanic, or Other (AHO) applicants may reflect the
contribution they would make to the racial diversity of the student body). As I have shown above,
even without the overall rating in my regression, I find no evidence of systematic bias in Harvard’s

125
126

Arcidiacono Report, Appendix B, Table B.6.8.
See workpaper.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 73

admissions process against Asian-American applicants.
155. Second, Prof. Arcidiacono suggests that the school support (teacher and guidance
counselor) ratings assigned by Harvard are biased against Asian-American applicants because he
observes that Asian-American applicants with the strongest academic qualifications (defined as those
in the top deciles (4-10) of the academic index) are less likely to receive strong school support ratings
relative to applicants of other races.127 Again, this conclusion depends on Prof. Arcidiacono’s
assumption that candidates who are strong on academic factors are also strong on non-academic
factors—an assumption that, as discussed above, is not supported by the available data. The teacher
and guidance counselor ratings reflect strength across both academic and non-academic dimensions.
Thus, the small gap between Asian-American and White applicants’ school support ratings may well
be attributable to the fact that Asian-American applicants tend on average to be weaker than White
applicants on the available measures of non-academic factors that Prof. Arcidiacono’s analysis
explicitly ignores by focusing on only deciles of the academic index.
156. Third, Prof. Arcidiacono also suggests that differences between the alumni overall and
personal ratings and Harvard’s admissions officers’ overall and personal ratings show that Harvard’s
personal and overall ratings are biased. But that argument once again depends on Prof. Arcidiacono’s
regression models of the ratings—which, again, are quite low in predictive accuracy and do not
reliably control for the many hard-to-measure factors that are likely very important to the
determination of the ratings. Second, the alumni and admissions-officer ratings are based on different
sources. An alumni personal rating reflects only the alumni interviewer’s brief interaction with the
applicant, whereas the personal rating assigned by Harvard admissions officers considers not just the
alumni interview (to the extent it has occurred before the rating is assigned, which is often not the
case) but also the candidate’s essays, teacher recommendations, secondary school report, and so on.
Alumni ratings are also much more generous in general. For example, 62% of applicants receive an
alumni personal rating of 1 or 2, while only 23% of the sample receive a personal rating of 1 or 2.128
Moreover, the personal ratings given by the Harvard admissions officers explain much more about
Harvard’s admissions decisions than the alumni interviewer personal ratings do. For Prof.
Arcidiacono’s expanded sample, the Pseudo R-Squared value of a model that controls for only the
personal rating is 0.19, while a model that controls for only the alumni personal rating has a Pseudo
R-Squared value of just 0.08.129 Given all of this, it is not particularly surprising that there exist
differences in the size of various coefficients across the two models.

127

Arcidiacono Report, p. 48.
See workpaper.
129
See workpaper.
128

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 74

5.3. Analysis of key subgroups of the data further contradicts SFFA’s claim of systematic bias
157. To further analyze SFFA’s claim that Harvard’s admissions process discriminates
against Asian-American applicants, I have also examined how the estimated effect of AsianAmerican ethnicity differs across time periods and subgroups of the applicant pool.
158. As discussed above in Section 5.1.6, a common methodological challenge when using
regression analysis to test for discrimination is that regressions typically cannot account for all
relevant factors that differ between two groups of people—in this case, between Asian-American and
White applicants. Further, as detailed in Sections 4 and 5 above, it is quite likely that both Prof.
Arcidiacono’s and my regression analyses do not fully account for the many non-academic factors
that are critical to admissions decisions in Harvard’s whole-person process (though my analysis
accounts for such factors more fully than Prof. Arcidiacono’s does). As a result, any gap that exists
between Asian-American and White applicants (or any group of applicants) may in fact reflect
average differences across race on factors not accounted for in the model.
159. One way to examine whether a racial disparity is attributable to bias is to assess whether
it is robust and consistent across subgroups and time periods in the data. If discrimination against
Asian-American applicants were the cause of the racial disparity in admission rates, one would
expect to see a systematic and robust racial difference in admission rates across all relevant
subgroups and time periods. By contrast, if the gap instead reflects differences across race in factors
that Harvard considers when making admission decisions—but that are missing from the model—it is
much more likely that the gap will vary across subgroups because, simply by chance, some subgroups
in the data are likely to be particularly strong or weak, in aggregate, on factors not accounted for in
the model.
160. In this section, I highlight a few patterns in the data that suggest the latter hypothesis is
more plausible. Specifically, as I discuss below, I find that the alleged effect of Asian-American
ethnicity is particularly small (and in fact positive rather than negative in most years—though
statistically insignificant) for two very large subgroups of Asian-American applicants—female
Asian-American applicants and Asian-American applicants applying from California dockets. I also
discuss how the fluctuation in the effect of Asian-American ethnicity on admissions from year to year
is inconsistent with the claim that Harvard’s admissions process is biased.
5.3.1. Asian-American ethnicity is associated with, if anything, a higher likelihood of admission for female
applicants
161. When my model is estimated only on female applicants, Asian-American ethnicity is

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 75

associated with a slightly higher probability of admission (though the difference is not statistically
significant). Exhibit 23 shows the results of my model for just the female sample. The effect of
Asian-American ethnicity is positive in five of six years and overall (and insignificant across the
board).

Average marginal effect of Asian-American ethnicity on admission is insignificant for AsianAmerican women

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Table shows the average marginal effect of race on admission, for Asian-American applicants relative to White applicants, using
Professor Arcidiacono’s corrected expanded sample. * indicates significance at the 5% level. Marginal effects are reported as percentage
point values.

162. This pattern is particularly interesting because Asian-American women are stronger on
non-academic dimensions than Asian-American applicants as a whole. Exhibit 24 shows that while
Asian-American men are stronger than Asian-American women on the academic rating, AsianAmerican women are stronger on two of the three non-academic ratings, including the personal
rating. Additionally, Asian-American women are more likely to be multi-dimensional (i.e. have three
or more ratings of 2 or better) than Asian-American men. In other words, Asian-American women are
a bit less strong on academics than Asian-American men, but make up for it by being relatively
stronger on other dimensions.
163. The fact that Asian-American female applicants are stronger on non-academic factors
than Asian-American male applicants, are more multi-dimensional than Asian-American male
applicants, and, if anything, may have a small advantage over White female applicants is consistent
with my interpretation that any unexplained gap between Asian-American and White applicants in

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 76

the models is in fact driven by average differences in unmeasured non-academic factors, rather than
by discrimination against Asian-American applicants.

Asian-American female applicants are stronger on non-academic measures and more multidimensional than Asian-American male applicants

Source: Arcidiacono Data
Note: Data are from Asian-American applicants to the classes of 2014 – 2019 in Professor Arcidiacono’s corrected expanded sample.
Ratings of 2- and above are classified as “2 or better” in this analysis. +/- rating designations are available in the data beginning with the
class of 2019.

5.3.2. Asian-American ethnicity is associated with a higher likelihood of admission for applicants on
California dockets
164. I also find that Asian-American ethnicity is associated with a slightly (though not
statistically significantly) higher probability of admission for applicants on California dockets —a
useful focal point for analysis because nearly 30% of Asian-American applicants are on California
dockets.
165. If Harvard’s admissions process sought to limit the number of Asian-American
applicants, it would be unlikely to favor Asian-American applicants relative to White applicants in
the region in which Asian-American applicants are most concentrated. Yet, when I estimate my logit

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 77

model on applicants from California dockets only, I find that Asian-American applicants are, if
anything, slightly more likely to be admitted than White applicants with the same observable
characteristics. This result does not suggest that Harvard is biased in favor of Asian-American
applicants on California dockets; it suggests, instead, that any perceived negative effect of AsianAmerican ethnicity in the national pool is more likely explained by factors omitted from the model
that vary across regions.

Admission rates for Asian-American applicants on California dockets are, if anything, higher than
those of White applicants once available factors are controlled for

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Sample consists of applicants to the classes of 2014 – 2019 in Professor Arcidiacono’s corrected expanded sample who are applying
from California dockets. Average marginal effects are calculated from the Card Model. * indicates significance at the 5% level. Marginal
effects are reported as percentage point values.

166. Exhibit 25 presents the estimated marginal effect of Asian-American ethnicity for
applicants on California dockets. That effect is positive in five of six years and overall (and
insignificant in all years). These findings provide further evidence that Harvard’s admissions process
exhibits no evidence of systematic discrimination against Asian-American applicants relative to
White applicants.
5.3.3. Evidence of the alleged disparity is also inconsistent across years
167. As noted above, my admissions model also exhibits year-by-year variation in the
estimated effect of Asian-American ethnicity on applicants’ likelihood of admission. For example, in
my preferred specification, the estimated effect of Asian-American ethnicity is negative in some
years and positive in others, with four of the six years exhibiting a positive (albeit still statistically

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 78

insignificant) association between Asian-American ethnicity and applicants’ likelihood of admission,
and two of the six years a negative (albeit still statistically insignificant) association.
168. Even in my sensitivity analysis in which I exclude the personal rating from the model,
the estimated effect of Asian-American ethnicity is not consistent from year to year. As noted above,
the estimated effect of Asian-American ethnicity is statistically significant only in the class of 2018
admissions cycle, and when that cycle is excluded, the average estimated effect across the other five
years is not statistically significant. Additionally, the estimated effect of Asian-American ethnicity
changes from positive to negative between years, with two of the six years being positive in this
model.
169. If Harvard’s admissions process were biased against Asian-American applicants
throughout this whole time period (as SFFA alleges), one would expect see a more consistent pattern
from year to year. The fact that the alleged “bias” fluctuates above and below zero from year to year
is more consistent with applicant pools from different years having a slightly different mix of
unmeasured, non-academic factors across ethnic groups that the model cannot perfectly account for,
than it is with the allegation of systematic bias against Asian-American applicants.
5.4. Conclusion
170. In this section, I have developed a statistical model that improves Prof. Arcidiacono’s
model by including in it a wide variety of factors that Harvard considers when making admissions
decisions and that Prof. Arcidiacono did not include in his model. My model also more accurately
reflects Harvard’s yearly admissions process in which applicants are compared only to other
applicants applying in the same year and not to applicants applying in other years.
171. I find no evidence of systematic bias against Asian-American applicants relative to
White applicants, after controlling for the many differences between these groups. While AsianAmerican applicants tend to have stronger academic qualifications, White applicants tend to be
stronger on non-academic dimensions. Prof. Arcidiacono’s model places a great deal of weight on
academic qualifications (including both the academic rating and the academic factors that inform that
rating), while omitting information related to each candidate’s life circumstances, including detailed
variables describing each high school and neighborhood in the data. When I add such measures to the
model to better account for the differences across all dimensions that Harvard considers (and for
which I have data), I find no statistically significant negative effect of Asian-American ethnicity
relative to White ethnicity on applicants’ probability of admission. Furthermore, the estimated effect
of Asian-American ethnicity relative to White ethnicity is positive in four of the six years.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 79

172. I also estimate a version of my model that assumes, as Prof. Arcidiacono alleges, that the
personal rating is biased against Asian-American applicants. I show that, even if the personal rating is
completely excluded from the model, there is at most weak evidence of a negative effect of AsianAmerican ethnicity on applicants’ likelihood of admission. In any event, Prof. Arcidiacono’s findings
of bias in the personal rating are weak for several reasons. His models of the various ratings (aside
from the academic rating) have low explanatory power. Additionally, he finds a significant and
positive effect of Asian-American ethnicity on two of the four profile ratings, which casts doubt on
whether the results actually reveal racial bias rather than simply the effect of unobservable factors
that differ across race. Collectively, the results of Prof. Arcidiacono’s ratings regressions are more
consistent with the absence of relevant difficult-to-quantify information from the database (or from
Prof. Arcidiacono’s models) than with systematic bias against Asian-American applicants.
173. Finally, I find that the alleged disparity between Asian-American and White admission
rates is inconsistent from subgroup to subgroup and from year to year. I find particularly weak
evidence of bias against female Asian-American applicants and Asian-American applicants on
California dockets. If anything, Asian-American applicants in those two groups are admitted at
slightly higher rates than comparable White applicants, controlling for relevant factors. Since 30% of
Asian-American applicants are on California dockets, and half are female, it is hard to reconcile those
findings with SFFA’s claim that Harvard intentionally and systematically discriminates against
Asian-American applicants on the basis of their race. I also find that the effect of Asian-American
ethnicity fluctuates from year to year, and is positive in four of six years. I am not aware of any basis
to believe that Harvard’s process was somehow biased in some years but not others. Again, these
results—taken together—suggest that any estimate of a negative effect of Asian-American ethnicity
at the national level reflects not racial discrimination but rather the effect of factors that are omitted
from the model because they cannot be quantified, and that vary across genders, regions, and years.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 80

6. AVAILABLE DATA DO NOT INDICATE THAT RACE IS A DETERMINATIVE FACTOR IN
ADMISSIONS AT HARVARD
174. In this section, I turn to a different research question regarding the importance of race in
Harvard’s admissions process. Using the regression model developed in Section 5 above, I explore
the size of the estimated effect of an applicant’s race or ethnicity on her likelihood of admission,
relative to the effect of the many other factors Harvard considers in its whole-person analysis.
175. Exhibit 26 summarizes the estimated average marginal effect of each racial category on
an applicant’s likelihood of admission. As already discussed in Section 5 above, the estimated effect
of Asian-American ethnicity is statistically indistinguishable from zero in every year. The estimated
effect of African-American ethnicity ranges from 5.20 percentage points to 7.43 percentage points,
and averages 6.12 percentage points, while the estimated effect of Hispanic and Other races (such as
Native American, and Hawaiian/Pacific Islander) ranges from 3.12 percentage points to 4.16
percentage points, and averages 3.73 percentage points.

Average marginal effect of race on the probability of admission

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Tables shows the estimated average marginal effect of race on admission, for each listed race, using Professor Arcidiacono’s
corrected expanded sample. * indicates significance at the 5% level. Marginal effects are reported in percentage point values.

176. In the remainder of this section, I offer a variety of analyses that provide context for how
important or unimportant race is relative to other factors in the admissions process. I find that (a) the
importance of race in explaining admissions decisions is substantially smaller than that of other key
factors Harvard considers; (b) even when race plays a role in admissions decisions, other applicant
attributes play a significant role as well; and (c) the effect of race is smaller than that of
individualized, unmeasured factors that are independent of race. All of these facts indicate that,

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 81

although race plays a role in admissions decisions, it is only one of a variety of factors considered and
is not the determinative factor.
6.1. Race is less important than other factors in admissions decisions
177. A starting point for estimating the effect of race relative to other factors in the
admissions process is to compare how effectively race explains admissions outcomes, relative to
other important factors Harvard considers in admissions. If race were a determinative factor, then
knowing an applicant’s race would allow one to predict with a high degree of certainty whether or not
the applicant is admitted.
178. Exhibit 27 reports the Pseudo R-Squared value for regressions of admissions outcomes
for the class of 2019 that include only racial categories as control variables, as well as regressions that
include only control variables other than race. As discussed above, Pseudo R-Squared is a statistic
that captures how well a variable (or set of variables) can explain admission decisions. It takes on
values from zero to one and is meant to approximate the share of the variation in actual admission
decisions that can be explained by the variables in the model. As shown in Exhibit 27, a regression
that includes only the variables for racial categories has a tiny Pseudo R-Squared value—just 0.002.
That means that race alone explains almost nothing about admissions outcomes. For comparison’s
sake, the profile ratings collectively explain a much larger proportion of the variability in admissions
outcomes (Pseudo R-Squared value of 0.33). School support ratings and alumni interview ratings
have Pseudo R-Squared values of 0.19 and 0.13, respectively. Even contextual factors that I include
in my model but that Prof. Arcidiacono does not include in his—such as College Board high school
and neighborhood variables, parental occupation, and intended career—explain more about
admissions decisions than race.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 82

Many factors better explain admission decisions than race

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Data are from applicants to the class of 2019 in Professor Arcidiacono’s corrected expanded sample.

179. In Table 7.1 of his report, Prof. Arcidiacono shows that, according to his model, many
Asian-American applicants with a 25% estimated likelihood of admission would have an estimated
likelihood of admission of over 90% if they were African-American.130 That is a misleading and
incomplete way to measure the relative importance of race for at least two reasons.
180. First, Prof. Arcidiacono has misleadingly selected a particular combination of applicant
characteristics for which the effect of race is largest. Exhibit 28 provides a fuller analysis that
examines the effect of race across all applicants, rather than a single example. It shows the average
estimated effect of race on probability of admission for African-American and Hispanic and Other
applicants (relative to White applicants) according to my year-by-year model for each decile of the
admissions index—a metric Prof. Arcidiacono has used in his analysis that measures the predicted
probability of admission absent consideration of race. As is clear, the higher likelihood of admission
associated with African-American ethnicity averages 13 percentage points or less for applicants in the
first nine deciles (that is, 90% of African-American applicants). For applicants in the highest decile
130

Arcidiacono Report, pp. 65–66.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 83

(the strongest applicants), it averages 47 percentage points. For applicants of Hispanic or Other (nonAsian) minority race, the estimated effect of race averages seven percentage points or less for
applicants in the first nine deciles and 29 percentage points for applicants in the highest decile.

Average marginal effect of race is small for the vast majority of AHO applicants

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Sample consists of applicants to the classes of 2014 – 2019 in Professor Arcidiacono’s corrected expanded sample. Deciles are
constructed by race based on the predicted probabilities of admission when the race factor is turned off. Marginal effects are calculated
relative to White applicants using Card year-by-year admissions model. Marginal effects are reported as percentage point values.

181. Second, the applicants with the largest estimated positive effect of race on their
likelihood of admission are the strongest applicants—i.e., those whose estimated likelihood of
admission is in the top 10% of the applicant pool absent consideration of race. Race is not a
“determinative” factor for such applicants, even if it has a significant positive effect on their
likelihood of admission, because they are strong in other respects. One way to see this fact is that
77% of AHO admitted students have at least two profile ratings of 2 or better.131 Applicants with at
least two profile ratings of 2 or better already have an admission rate of 23%.132

131
132

See workpaper.
See workpaper.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 84

6.2. Race is less important than unmeasured, individualized factors
182. It is also possible to compare the effect of race in Harvard’s admissions process to that of
individualized, unmeasured factors—that is, factors not captured by the model. One way to do that is
to examine the predicted probability of admission for each applicant and compare that to the actual
admission decision for the applicant.
183. As discussed in Section 5.1.6 above, if the model generates a predicted probability of
admission close to zero for a candidate who was rejected or a predicted probability of admission
close to one for a candidate who was admitted, one can conclude that the variables in the model allow
the researcher to be very confident about that applicant’s admissions outcome. If, however, the model
generates a predicted probability of admission of, say, 0.10 for a given candidate who was actually
admitted, one can conclude that the variables in the model do not allow the researcher to explain with
any degree of certainty why the applicant was admitted. In other words, it is the unquantifiable
factors that ultimately determined whether the candidate was admitted. More generally, one can
quantify the importance of such factors by using the “error” term from the model—that is, the actual
admission outcome (1=admitted) minus the estimated admission outcome (0.10)—which measures
the relative importance of factors specific to that individual that are not included in the model.
184. To give a concrete example, consider an applicant who is not admitted and whose SAT
scores and GPA are so low that it is essentially impossible for the applicant to be admitted. For such
an applicant, one can conclude that unquantified factors not present in the model are not a major
factor in the decision—the observable information on academic achievements is sufficient to
understand the decision. The applicant’s estimated likelihood of admission will be close to zero, and
the applicant’s actual admissions outcome will be zero (not admitted), so the error term will be very
small. On the other hand, consider an applicant with an academic rating of 3, an extracurricular rating
of 2, and a personal rating of 2. Suppose the model predicts the applicant has a 40% chance of
admission, and ultimately she is in fact admitted. What I conclude from such information is that other
factors that are specific to that candidate that are not observed in the model explain 60% of the
outcome—the difference between the applicant’s actual likelihood of admission (100%) and her
estimated likelihood of admission according to the model (40%).
185. By comparing the marginal effect of race for any given applicant to the error in the
model, it is possible to compare the role of race in the admissions process to the role played by
unobserved factors that are independent of race. Exhibit 29 shows that the portion of the admissions
decision attributable to unobserved characteristics of each individual applicant is greater than the
effect of race for 100% of Asian-American applicants, for 94% of African-American applicants, and
for 96% of Hispanic or Other applicants. In other words, in nearly all cases, race matters less to an

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 85

applicant’s admissions outcome than individualized factors that are not in the model.

Average marginal effect of race is small compared to importance of unobserved characteristics

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Data are from applicants to the classes of 2014 – 2019 in Prof. Arcidiacono’s corrected expanded sample. Absolute deviations and
marginal effects are reported as percentage point values. Table shows the average marginal effect of race on admission relative to White
applicants. Absolute deviation is computed by taking the absolute value of the difference between the actual admitted status and the
predicted probability of each applicant. Absolute deviation is compared with the absolute value of the marginal effect for each applicant.

186. Exhibit 30 shows the effect of race relative to other observed and unobserved factors
focusing only on applicants who were admitted to Harvard. Each bar shows the relative effect of
three different groups of factors in the model: race, observable factors other than race, and
unobservable factors that are specific to individuals and not captured in the model. Even for AfricanAmerican admitted students, race explains less than half (42%) of the variability in admissions
outcomes. For Hispanic or Other minority race applicants, race explains only 26% of the variability
in admissions outcomes. In other words, non-race factors play a large role.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 86

Non-racial factors play the dominant role in admissions decisions

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Data are from students admitted to the classes of 2014 – 2019 in Prof. Arcidiacono’s corrected expanded sample. Average effect of
race is computed as the average marginal effect of race on admission relative to White applicants. Average effect of non-race observable
characteristics is computed as the average difference between the predicted probability and the marginal effect of race. Average effect of
unobservable characteristics is computed as the mean absolute deviation. Absolute deviation is computed by taking the absolute value of
the difference between the actual admitted status (0 or 1) and the predicted probability of admission for each applicant.

6.3. Prof. Arcidiacono’s claim about a “floor” for the admission rate of African-American applicants is
not supported by available data
187. Prof. Arcidiacono also asserts that, starting with the class of 2017, Harvard intentionally
sought to match the admission rate for African-American applicants to the admission rate for all
applicants. That assertion is not supported by available data.
188. Prof. Arcidiacono claims that the impetus for this practice was that, beginning with the
class of 2017, “Harvard adopted a new methodology for coding race and ethnicity that was consistent
with federal standards for reporting of race and ethnicity.”133 Under that methodology—known as the
Integrated Postsecondary Education Data System (IPEDS) methodology—students who identify as

133

Arcidiacono Report, p. 27.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 87

African-American and another race are counted as “multiracial,” not as African-American. The
IPEDS methodology contrasts with Harvard’s historical method for classifying race (the “Old
Methodology”), which categorizes any applicant who identifies as African-American as AfricanAmerican, whether or not that applicant also identifies with another racial or ethnic group. It also
contrasts with Harvard’s current preferred method for classifying race (the “New Methodology”),
which counts applicants in as many racial categories as they choose to identify on their applications
(so that an applicant who identified as African-American and, say, Asian-American would be counted
in both categories). Professor Arcidiacono argues that the IPEDS methodology “prompted concern at
Harvard that the new reporting would understate the number of African-American admits to
Harvard.”134 He argues that this concern drove Harvard to impose a floor on the African-American
admission rate.
189. In this section I consider both the substance of Prof. Arcidiacono’s claim, as well as
whether the data are consistent more broadly with the idea that Harvard is imposing a floor on the
admission rate for African-American applicants.
190. As an initial matter, Prof. Arcidiacono does not explain why Harvard would care about
manipulating the admission rate of candidates who are African-American according to the IPEDS
methodology. First, Harvard does not publicly release admission rates by race, so it is unclear why
Harvard would be sensitive to the public perception of its admission rates by race.135 Second, when
Harvard publicly announces the racial composition of the admitted and matriculating classes (as
opposed to the admission rates), it does so using its own definitions of race—first the “Old
Methodology” (used since at least the class of 1980) and now the “New Methodology” discussed
above. Harvard does not publicly report racial statistics using the IPEDS methodology.136 Finally, the
IPEDS methodology was not new in the class of 2017 admissions cycle; in accordance with federal
reporting requirements, Harvard had already been reporting race to the government using the IPEDS

134

Arcidiacono Report, p. 28.
Fitzsimmons Deposition, pp. 453–454 (“Q. Does Harvard publicly report its admission rate by ethnicity? A. No.”).
136
Yong Deposition, p. 138 (“Q. But you don’t use the IPEDS methodology? A. Not for press releases.”); Fitzsimmons
Deposition, pp. 100–101 (“Q. When Harvard reports its results in the Harvard Gazette, does it use the IPEDS
methodology or the new methodology to describe the ethnic characteristics of the class? A. It would use the new
methodology.”); Table, Aggregate applicant data 1980 – 2018, HARV00023177 – 8.
.
135

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 88

methodology since the 2010–11 school year (entering class of 2014), three years earlier.137
191. Furthermore, Prof. Arcidiacono’s selective focus on the admission rate as defined using
the IPEDS methodology presumably reflects the fact that admission rates calculated using Harvard’s
own preferred methodologies do not show the effect he regards as problematic—in other words, the
admission rate for African-American candidates (as defined using the New Methodology and Old
Methodology) does not match the overall admission rate. For example, for the class of 2016, the
African-American admission rate based on both the “New Methodology” and the “Old Methodology”
was nearly a half point below the admission rate of all other applicants.138
192. In addition, if Harvard had lowered its admission standards to ensure an artificially high
admission rate for African-American applicants, one might expect to see a decline in the relative
quality of African-American admitted students starting in the class of 2017. No such decline
occurred.139 Further, the estimated positive effect of African-American ethnicity on applicants’
likelihood of admission (based on my regression analysis in Exhibit 26) is generally smaller for
applicants to the classes of 2017 to 2019 than for applicants to the classes of 2014 and 2015. If
Harvard implemented a floor for the admission rate of African-American students starting with the
class of 2017, the regression model should show a larger positive association between AfricanAmerican ethnicity and likelihood of admission in later years than in prior years—not a smaller one.
193. Finally, Harvard has produced aggregate admission data by race, using its Old
Methodology, that extend back to 2000. Using that aggregate data I can examine the fluctuations
from year to year in admissions decisions by race, and assess whether such fluctuations are in any
way consistent with a “floor” in admissions for African-American applicants, and/or a substantive
change starting with the class of 2017.
194. Exhibit 31 through Exhibit 34 report the year-to-year fluctuations in the racial
composition of the admitted class. There is no evidence that Harvard has sought to achieve a
consistent proportion of African-American students. To the contrary, the share of admitted students
who are African-American fluctuates considerably from year to year, by as much as 14%. Similar
patterns exist for all races. For example, despite SFFA’s claims that Harvard seeks to limit the share
of its class that is Asian-American, Exhibit 32 shows that the share of the class that is AsianAmerican has fluctuated significantly.

Harvard Memo, “A Note on the Collection and Reporting of Data on Race and Ethnicity,” HARV00065450 – 52 at
HARV00065450 – 51; “Resources for Implementing Changes to Race/Ethnicity Reporting in IPEDS,” National Center
for Education Statistics, available at https://nces.ed.gov/ipeds/Section/Resources, accessed December 1, 2017.
138
See workpaper.
139
See workpaper.
137

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 89

The fraction of admitted students who are White fluctuates over time

Source: HARV00001848 – 1850; Augmented Arcidiacono Data
Note: Sample consists of domestic applicants who are classified as White under the Old Methodology.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 90

The fraction of admitted students who are Asian-American fluctuates over time

Source: HARV00001848 – 1850; Augmented Arcidiacono Data
Note: Sample consists of domestic applicants who are classified as Asian-American under the Old Methodology.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 91

The fraction of admitted students who are African-American fluctuates over time

Source: HARV00001848 – 1850; Augmented Arcidiacono Data
Note: Sample consists of domestic applicants who are classified as African-American under the Old Methodology.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 92

The fraction of admitted students who are Hispanic or Other fluctuates over time

Source: HARV00001848 – 1850; Augmented Arcidiacono Data
Note: Sample consists of domestic applicants who are classified as Hispanic or Other under the Old Methodology.

6.4. Conclusion
195. As detailed above, I find little evidence that race is a determinative factor in the
admissions process. Specifically, I find that race explains much less about applicants’ likelihood of
admission than numerous other factors Harvard considers.
196. I also examine Prof. Arcidiacono’s claim that the marginal effect of race can be quite
large for certain individual candidates. I find that the marginal effect of race averages 13 percentage
points or less for 90% of African-American applicants and averages 7 percentage points or less for
90% of Hispanic or Other applicants. And for the small number of applicants for whom race plays a
more significant role, other non-race factors also substantially affect the applicants’ likelihood of
admission. Further, I find that the average marginal effect of race is less than that of individualized,
unmeasured factors that are independent of race. For admitted AHO applicants in particular, race
explains only about 34% of the variation in admissions outcomes.140

140

See workpaper.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 93

197. Finally, I also consider Prof. Arcidiacono’s claim that Harvard began manipulating its
admission rate for African-American applicants—as defined using the IPEDS methodology—starting
with the class of 2017 admissions cycle. It is highly implausible that Harvard would attempt to
manipulate that particular statistic, which it does not release to the public. And, indeed, a review of
the data using Harvard’s preferred methods for categorizing applicants by race does not show the
effect Prof. Arcidiacono observes.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 94

7. ANALYSIS OF POTENTIAL RACE-NEUTRAL ALTERNATIVES
198. In this section, I address the question of how the racial composition and other attributes
of Harvard’s admitted class would be expected to change if Harvard stopped considering race and
instead pursued a variety of race-neutral ways of seeking to increase the racial diversity of its
admitted class. My findings indicate that, to the extent race-neutral practices can enable Harvard to
achieve racial diversity, they would do so only by altering other characteristics of the admitted class
that I understand matter to Harvard.
199. I begin by surveying the academic literature on race-neutral alternatives—including
papers suggested by SFFA and its expert, Richard Kahlenberg—in order to identify admissions
practices that have been posited to be effective at increasing racial diversity. I also discuss the
literature evaluating these practices and whether they could work at a highly selective university like
Harvard. Next, I use Harvard’s admissions data to demonstrate how various potential race-neutral
admissions practices would be expected to affect the racial composition and other attributes of
Harvard’s admitted class. Finally, I discuss Mr. Kahlenberg’s analysis of race-neutral alternatives.
200. I reach several conclusions. First, Harvard already engages in extensive race-neutral
efforts to increase the racial diversity of its student body. Second, consistent with the academic
literature, I find that Harvard’s use of additional race-neutral efforts to increase racial diversity would
not likely enable it to achieve a comparably diverse class if it did not consider race in admissions. To
the extent that the use of race-neutral alternatives did enable Harvard to achieve a comparably diverse
class, it would likely have a substantial deleterious effect on the quality of the admitted class along
many dimensions. Finally, I find that Mr. Kahlenberg’s proposed race-neutral alternatives do not
depart from this pattern—that is, they either are ineffective at generating a racially diverse class, or
would significantly alter the composition of the admitted class along other dimensions.
7.1. Race-neutral alternatives identified in academic literature and by SFFA
201. To develop a comprehensive list of race-neutral alternatives that Harvard could consider,
I first considered the race-neutral alternatives identified by SFFA and its expert, Mr. Kahlenberg,
then explored the academic literature for additional race-neutral alternatives that SFFA potentially
overlooked. In this section, I summarize the race-neutral alternatives I found.
7.1.1. Race-neutral alternatives identified by SFFA and its expert
202. In Section X of the Complaint and in the Kahlenberg Report, SFFA and Mr. Kahlenberg
list a series of race-neutral alternatives that have been identified in academic literature, and that they

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 95

believe would allow Harvard to achieve racial diversity without considering race.
203. First, SFFA and Mr. Kahlenberg suggest that Harvard should eliminate admissions
practices that supposedly diminish racial diversity—namely (1) the consideration of whether an
applicant’s parents attended Harvard or Radcliffe (i.e., whether the applicant is a “lineage applicant”),
(2) the consideration of whether an applicant’s parents are members of Harvard’s faculty or staff,
(3) the practice of offering applicants deferred admission to a class subsequent to the one for which
they applied, (4) the alleged consideration of whether an applicant’s family has contributed or has the
ability to contribute financially to Harvard, (5) the practice of tracking the admissions status of
candidates of particular interest to Harvard’s Dean and Director of Admissions, and (6) the practice
of Early Action admissions. Mr. Kahlenberg also suggests that removing consideration for recruited
athletes could help foster racial diversity, though he does not include this practice in his preferred
simulation (explaining that it “is sometimes perceived as radical”).141
204. Second, SFFA and Mr. Kahlenberg suggest that Harvard should increase the
consideration it affords in the admissions process to students of lower socioeconomic status. Mr.
Kahlenberg also suggests that, to do so, Harvard should make available to admissions officers
whatever information its Financial Aid Office may possess about applicant’s family income and
wealth.142
205. Third, SFFA and Mr. Kahlenberg suggest that Harvard should increase the financial aid
it offers, on the theory that doing so would attract more applicants and matriculants of lower
socioeconomic status.143
206. Fourth, SFFA and Mr. Kahlenberg suggest that Harvard adopt geography-based
preferences, such as a “percent plan” under which it would admit the top students from each high
school or each ZIP code.144
207. Fifth, SFFA and Mr. Kahlenberg suggest that Harvard increase its efforts to recruit a

141

Kahlenberg Report, pp. 31–34, p. 41, and p. 46.
Kahlenberg Report, pp. 23–29.
143
Kahlenberg Report, pp. 29–31.
144
Kahlenberg Report, pp. 36–39.
142

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 96

diverse applicant pool.145
208. Sixth, SFFA and Mr. Kahlenberg suggest that Harvard could increase racial diversity by
accepting more transfer applicants, particularly from community colleges.146
7.1.2. Additional race-neutral alternatives identified in the literature
209. I also reviewed the academic literature discussing race-neutral alternatives, and this
review indicates that SFFA’s list of race-neutral alternatives is generally comprehensive. One raceneutral strategy for increasing racial diversity that SFFA does not mention but that is discussed in the
academic literature is reducing or eliminating consideration of standardized test scores. That strategy
is predicated on the theory that standardized tests may advantage students who attend better schools
and have more resources for test preparation, who are more likely to be White or Asian-American.147
For completeness, I include this practice in my analyses below.
7.2. Academic research indicates that race-neutral alternatives diminish universities’ ability to select for
quality
210. Many academics have studied the efficacy of race-neutral alternatives in generating a
high-quality, racially diverse student body without considering race in the admissions process. While
there is general agreement that race-neutral alternatives can help increase racial diversity relative to
an admissions regime that does not consider race, there is little empirical evidence that race-neutral
alternatives have produced diverse student bodies comparable to those attained under race-conscious
regimes at selective institutions, where researchers note that race-neutral policies may be less
effective.148 Furthermore, the literature indicates that the replacement of race-conscious admissions
with race-neutral alternatives introduces an unavoidable tradeoff between the quality and racial
145

Kahlenberg Report, pp. 39–40.
Kahlenberg Report, pp. 41–42.
147
John Brittain and Benjamin Landy, “Reducing Reliance on Testing to Promote Diversity,” in The Future of Affirmative
Action, ed. Richard Kahlenberg (Century Foundation Press, 2014), pp. 160–174 at p. 161; Anthony P. Carnevale, Stephen
J. Rose, and Jeff Strohl, “Achieving Racial and Economic Diversity with Race-Blind Admissions Policy,” in The Future
of Affirmative Action, ed. Richard Kahlenberg (Century Foundation Press, 2014), pp. 187–202 at pp. 189 and 193.
148
Halley Potter, “Transitioning to Race-Neutral Admissions: An Overview of Experiences in States Where Affirmative
Action Has Been Banned,” in The Future of Affirmative Action, ed. Richard Kahlenberg (Century Foundation Press,
2014), pp. 75–90 at pp. 88–89; Thomas J. Kane, “Racial and Ethnic Preferences in College Admissions,” Ohio St. Law
Journal 59, 1998, pp. 971–996 at pp. 972 and 992; Sean Reardon, Rachel Baker, and Daniel Klasik, “Race, income, and
enrollment patterns in highly selective colleges, 1982-2004,” Center for Education Policy Analysis, Stanford University,
2012, pp. 1–25 at p. 4.
146

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 97

diversity of an admitted class. In essence, the literature concludes, universities that attempt to achieve
racial diversity without considering race have lesser ability to choose the highest-quality class than if
they were able to consider race.149
7.2.1. Race-neutral alternatives do not achieve the same level of racial diversity as race-consciousness at
selective universities
211. Economic research on alternative admissions policies firmly supports the effectiveness
of race-conscious admissions in achieving racial diversity at selective institutions. The economic
literature studying attempts to produce racial diversity without considering race tends to focus on the
efficacy of race-neutral alternatives in generating a substantial fraction of African-American and
Hispanic students, largely because those are the groups whose representation falls most significantly
when universities remove consideration of race from admissions.
212. Thomas Espenshade and Chang Chung (2005), for example, conduct simulations for
three elite private research universities (which they do not identify). They find that eliminating the
consideration of race in admissions would notably reduce the share of African-American and
Hispanic students among admitted students, and that consideration of lineage and athletic-recruit
status has little effect on African-American and Hispanic representation.150
213. Economic research regarding bans on race-conscious admissions in Texas, Florida,
California, and Washington suggests that those bans adversely affected racial diversity, especially at
more selective schools.151 A separate analysis of the California ban studied the efficacy of a battery of
alternative admissions practices, including a preference for applicants of low socioeconomic status.

149

Jimmy Chan and Erik Eyster, “Does Banning Affirmative Action Lower College Student Quality?,” American
Economic Review 93(3), 2003, pp. 858–872 at pp. 858–856; Mark Long, “Is There a ‘Workable’ Race-Neutral
Alternative to Affirmative Action in College Admissions?,” Journal of Policy Analysis and Management 34(1), 2015, pp.
162–183 at p. 167; Mark Long, “The Promise and Peril for Universities Using Correlates of Race in Admissions in
Response to the Grutter and Fisher Decisions,” ETS White Paper, 2015, pp. 1–31 at p. 13; Glenn Ellison and Parag
Pathak, “The Efficiency of Race-Neutral Alternatives to Race-Based Affirmative Action: Evidence from Chicago’s Exam
Schools,” NBER Working Paper #22589, 2016, pp. 1–59 at p. 51; Roland Fryer, Glenn Loury, and Tolga Yuret, “An
Economic Analysis of Color-Blind Affirmative Action,” The Journal of Law, Economics, & Organization 24(2), 2007,
pp. 319–355 at p. 1; Roland Fryer and Glenn Loury, “Affirmative Action and Its Mythology,” The Journal of Economic
Perspectives 19(3), 2005, pp. 147–162 at pp. 150–153.
150
Thomas Espenshade and Chang Y. Chung, “The opportunity cost of admission preferences at elite universities,” Social
Science Quarterly 86(2), 2005, pp. 293–305 at p 298.
151
Peter Hinrichs, “The effects of affirmative action bans on college enrollment, educational attainment, and the
demographic composition of universities,” The Review of Economics and Statistics 94(3), 2012, pp. 712–722 at p. 712.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 98

None of the alternative practices analyzed was able to produce a student body with diversity
comparable to that predating the ban on race-conscious admissions practices.152
7.2.2. Much of the literature focuses on universities far less selective than Harvard
214. While some universities have used race-neutral alternatives with moderate success in
achieving racial diversity, those universities tend to be far less selective than Harvard, making it
easier for them to attract applicants who do not reduce the quality or alter the character of the student
body. Halley Potter, a colleague of Mr. Kahlenberg on whose work he relies, studied eleven flagship
state universities that were barred from using race in admissions. Of those eleven schools, seven were
able to achieve African-American and Hispanic enrollment comparable to that attained before the
ban; four were not. Importantly, the three most selective schools in the sample—UC-Berkeley,
Michigan, and UCLA, the schools most similar to Harvard—were among the four schools not able to
attain pre-ban levels of representation for African American and Hispanic students.153 As Potter
explains, scholars have yet to identify race-neutral strategies that work well for selective institutions:
Selective colleges have a smaller pool of qualified applicants to begin
with, and these applicants are more likely to be considering a variety of
in- and out-of-state college options. As a result, selective colleges may
face greater challenges in terms of recruiting additional applicants from
underrepresented demographics… [I]dentifying effective diversity
strategies for selective campuses under race-neutral admissions is an
important area for future research.154
215. Instead of focusing on the efficacy of race-neutral alternatives at selective institutions,
Mr. Kahlenberg chooses to highlight the handful of large, less selective public schools that (he
argues) were able to employ race-neutral alternatives to attain diverse classes comparable to those
before the consideration of race was banned. Examples include Texas A&M, the University of
Washington, the University of Nebraska, the University of Arizona, and the University of Georgia.155

152
Daniel Koretz, Michael Russell, Chingwei David Shin, Cathy Horn, Kelly Shasby, “Testing and Diversity in
Postsecondary Education: The Case of California,” Education Policy Analysis Archives 10(1), 2002, pp. 1–39 at pp. 27–
28.
153
The fourth university that failed to regain pre-bar levels of representation for both African-American and Hispanic
students was the University of New Hampshire. (Halley Potter, “Transitioning to Race-Neutral Admissions: An Overview
of Experiences in States Where Affirmative Action Has Been Banned,” in The Future of Affirmative Action, ed. Richard
Kahlenberg (Century Foundation Press, 2014), pp. 75–90 at p. 89.)
154
Halley Potter, “Transitioning to Race-Neutral Admissions: An Overview of Experiences in States Where Affirmative
Action Has Been Banned,” in The Future of Affirmative Action, ed. Richard Kahlenberg (Century Foundation Press,
2014), pp. 75–90 at p. 89.
155
Kahlenberg Report, p. 6.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 99

But those schools’ experience sheds little light on how race-neutral alternatives would fare at
Harvard, a far smaller and far more selective institution. As I show below, any strategies used by
larger and less selective universities, such as percent plans or increased transfers from community
colleges, are likely to generate a pool of applicants who are less qualified than Harvard’s current
applicants.
7.2.3. The literature shows that an increased preference for applicants of lower socioeconomic status can
achieve racial diversity only at the cost of reducing the quality of the admitted class on a range of
dimensions that I understand Harvard considers important
216. Mr. Kahlenberg places heavy emphasis on the idea that universities can achieve racial
diversity without considering race by according a significant admissions preference to applicants of
low socioeconomic status (SES). In my view, however, the literature does not support that
conclusion. (Nor, as I will discuss later, do simulations using Harvard’s data.)
217. It is widely understood as a matter of economic theory that if a university is forced to
target an imperfect correlate of race to achieve racial diversity, it is less able to choose the highestquality class than if it considered race directly.156 Giving a strong admission preference to low-SES
candidates can indirectly generate racial diversity because some SES measures are correlated with
race. But because SES is not a perfect proxy for race, universities must place a significant weight on
SES measures to obtain substantial racial diversity—above and beyond what would be optimal for
creating a high-quality class in other dimensions. Even when the link between SES and race is strong,
this high degree of emphasis on SES factors can significantly alter the characteristics of the admitted
class.
218. Mr. Kahlenberg cites literature selectively in attempting to diminish the well supported
principle that targeting correlates of race will always be a more costly way to generate racial diversity
(in terms of the costs it imposes on other attributes of the admitted class) than considering race itself.
156

Jimmy Chan and Erik Eyster, “Does Banning Affirmative Action Lower College Student Quality?,” American
Economic Review 93(3), 2003, pp. 858–872 at pp. 858–859; Mark Long, “Is There a ‘Workable’ Race-Neutral
Alternative to Affirmative Action in College Admissions?,” Journal of Policy Analysis and Management 34(1), 2015, pp.
162–183 at p. 167; Mark Long, “The Promise and Peril for Universities Using Correlates of Race in Admissions in
Response to the Grutter and Fisher Decisions,” ETS White Paper, 2015, pp. 1–31 at p. 13; Glenn Ellison and Parag
Pathak, “The Efficiency of Race-Neutral Alternatives to Race-Based Affirmative Action: Evidence from Chicago’s Exam
Schools,” NBER Working Paper #22589, 2016, pp. 1–59 at p. 51; Roland Fryer, Glenn Loury, and Tolga Yuret, “An
Economic Analysis of Color-Blind Affirmative Action,” The Journal of Law, Economics, & Organization 24(2), 2007,
pp. 319–355 at pp. 319–320; Roland Fryer and Glenn Loury, “Affirmative Action and Its Mythology,” The Journal of
Economic Perspectives 19(3), 2005, pp. 147–162 at pp. 150–153.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 100

For example, Mr. Kahlenberg cites the work of Richard H. Sander and Aaron Danielson, who (in Mr.
Kahlenberg’s words) suggest that “richer measures of socioeconomic status … significantly increased
the correlation between race and socioeconomic status and the racial dividend of class-based
affirmative action.”157 But Mr. Kahlenberg fails to note that these same authors also assert that “[i]t is
axiomatic that no race-neutral factor or system can be as efficient as using race itself to achieve racial
diversity through an admissions program … The high academic costs of the larger SES preferences in
these models would, we think, render it unpalatable to most selective schools.”158
219. Mr. Kahlenberg also cites Matthew N. Gaertner’s 2014 study of race-neutral alternatives
at the University of Colorado to support the claim that preferences for applicants of low
socioeconomic status can “achieve even more racial diversity than using racial preferences.”159 But
Mr. Kahlenberg neglects Gaertner’s warning that such policies are complicated to implement and
may lower the academic quality of the admitted class and the likelihood of success for admitted
students.160
220. Mr. Kahlenberg draws on the work of Anthony P. Carnevale, Stephen J. Rose, and Jeff
Strohl, who simulate several race-blind admissions regimes. The authors do find that these
approaches can produce racial diversity, but only “if elite colleges are willing to risk lower average
test scores … and thereby lower graduation rates.”161
221. Mr. Kahlenberg also cites work by Anthony P. Carnevale and Stephen J. Rose to support
his claim that “top universities could nearly quadruple the proportion of students from the bottom

157

Kahlenberg Report, p. 19.
Aaron Danielson and Richard H. Sander, “Thinking Hard About ‘Race-Neutral’ Admissions,” University of Michigan
Journal of Law Reform 47(4), 2014, pp. 967–1020, at pp. 968 and 995.
159
Kahlenberg Report, p. 12.
160
Matthew N. Gaertner, “Advancing College Access with Class-Based Affirmative Action,” in The Future of Affirmative
Action, ed. Richard Kahlenberg (Century Foundation Press, 2014), pp. 175–186 at pp. 183–184 (“Table 114.5 suggests
that on average, class-based admits can be expected to perform worse in college than typical undergraduates…These
patterns should not be terribly surprising, given that class-based admits are ‘borderline’ applicants—students on the cusp
of admission whose academic credentials are not stellar, and whose personal qualities weigh more heavily in an
admissions decision[]” and “Across outcomes, strictly overachieving class-based admits can be expected to perform quite
well—better, in fact, than typical undergraduates. The forecasts for strictly disadvantaged admits, however, are not as
encouraging. Their GPAs, graduation rates, and earned credit hours lag far behind the baseline.”).
161
Anthony P. Carnevale, Stephen J. Rose, and Jeff Strohl, “Achieving Racial and Economic Diversity with Race-Blind
Admissions Policy,” in The Future of Affirmative Action, ed. Richard Kahlenberg (Century Foundation Press, 2014), pp.
187–202 at p. 188.
158

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 101

socioeconomic half … without any change in graduation rates.”162 But he fails to note that in the
simulation he references, African-American representation fell by a third, suggesting that the
simulated admissions regime was ineffective at producing racial diversity even if it generated
socioeconomic diversity.163 Indeed, Carnevale and Rose concluded that “ultimately there is no better
way to guarantee a certain level of racial diversity than by employing race per se” and that “[w]hile
socioeconomic preferences help produce some racial diversity, a credible procedure that can
reproduce the level of racial diversity that exists in society today without purposely singling out
African Americans and Hispanics at some point in the selection process has yet to be found.”164
222. Finally, Mr. Kahlenberg also cites the work of Sigal Alon, highlighting a set of Alon’s
simulations, which, he argues, show that “if the most selective 115 American universities instituted
broad reform—including effectively eliminating lineage, athletic, and racial preferences—a
socioeconomic boost ‘could not only replicate the current level of racial and ethnic diversity at elite
institutions but even increase it.’”165 But Alon’s simulations do not consistently show that AfricanAmerican and Hispanic representation would meet or exceed the levels achieved by considering race.
Furthermore, in the one simulation where the fraction of African-American and Hispanic admitted
students exceeds the levels achieved by considering race, Alon notes that the “price” of this racial
diversity “would be a decline in academic selectivity.”166 He also notes that those policy changes
would substantially increase the cost of providing financial aid.167
223. Far from buttressing his claim that preferences for low-SES applicants can enable
selective colleges to increase racial diversity without harming the quality of their student bodies, the
literature Mr. Kahlenberg cites specifically highlights the challenges and costs of such policies for a
selective school like Harvard.
7.2.4. Conclusion
224. In sum, my review of the literature indicates that while race-neutral alternatives can be
used to increase racial diversity relative to a regime that does not consider race at all, (1) they
typically do not produce diverse student bodies comparable to those attained using race-conscious
162

Kahlenberg Report, p. 14.
Anthony P. Carnevale and Stephen J. Rose, “Socioeconomic Status, Race/Ethnicity, And Selective College
Admissions,” in America’s Untapped Resource: Low Income Students, ed. Richard Kahlenberg (Century Foundation
Press, 2004), pp 101–156 at p. 148.
164
Anthony P. Carnevale and Stephen J. Rose, “Socioeconomic Status, Race/Ethnicity, And Selective College
Admissions,” in America’s Untapped Resource: Low Income Students, ed. Richard Kahlenberg (Century Foundation
Press, 2004), pp 101–156 at pp. 150 and 153.
165
Kahlenberg Report, p. 13.
166
Sigal Alon, Race, Class, and Affirmative Action (New York, NY: Russell Sage Foundation, 2015), pp. 254–256.
167
Sigal Alon, Race, Class, and Affirmative Action (New York, NY: Russell Sage Foundation, 2015), p. 256
163

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 102

admissions at selective institutions, and (2) both as a theoretical matter and in practice, they reduce
the quality of the admitted class. As I show in the remainder of this section, my analysis of Harvard’s
admissions data bear out the consensus in the literature: Harvard is unlikely to be able to achieve a
comparably diverse student body without considering race and without decreasing the overall quality
of the admitted class on a variety of dimensions.
7.3. Analysis of race-neutral alternatives using Harvard’s admissions data
225. In this section, I evaluate how Harvard’s class would change under race-neutral
alternatives identified in the academic literature discussed above, including those alternatives
suggested by the Complaint and Mr. Kahlenberg. I employ two methodological approaches in my
analysis.
226. First, I simulate how the use of certain race-neutral alternatives would be expected to
change the demographic and other characteristics of the admitted class. Consistent with the broader
academic literature, I find that any of the race-neutral alternatives proposed by SFFA or Mr.
Kahlenberg that would achieve a class with comparable ethnic and racial diversity would do so only
by changing other attributes of the class in ways that I understand matter to Harvard.
227. Second, for race-neutral practices that Harvard has already employed or experimented
with in the past (i.e., increased financial aid and the elimination of Early Action admissions), I
examine historical data to assess whether further changes could help achieve racial diversity. I find
that (a) eliminating Early Action is unlikely to foster additional racial diversity, and (b) given
Harvard’s current financial aid and recruiting practices, further expansions in financial aid and
recruiting are unlikely to increase racial diversity.
7.3.1. Eliminating consideration of race in the admissions process
228. To simulate the effect of removing consideration of race from the admissions process, I
begin by estimating my preferred year-by-year model (developed in Section 5) for applicants to the
class of 2019. I then turn off the estimated coefficients on the race variables, allowing me to simulate
what class would be admitted if Harvard did not consider race in the admissions process. In my
simulation, the share of African-American students in the admitted class would drop from 14% to
6%. The fraction of Hispanic or Other students would fall from 14% to 9%. The fraction of admitted
students who are Asian-American would rise from 24% to 27%. And the fraction of admitted
students who are White would rise from 40% to 48%.168

168

See Exhibit 36 for an illustration of these changes.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 103

7.3.2. Eliminating deferred admission and consideration of whether an applicant is a lineage applicant, a
child of Harvard faculty or staff, a recruited athlete, or on the Dean’s or Director’s interest lists
229. Mr. Kahlenberg suggests that one way a school like Harvard could attempt to increase
racial diversity would be to eliminate admissions practices that allegedly benefit White applicants.
The specific practices identified by Mr. Kahlenberg and addressed in his simulations include:
Harvard’s practice offering deferred admission to a small group of candidates, conditional on their
taking a year off before matriculating; consideration of whether an applicant’s parents attended
Harvard or Radcliffe (i.e., whether the applicant is a “lineage” applicant); consideration of whether an
applicant is the child of Harvard faculty or staff; consideration of whether an applicant is a recruited
athlete; and the use of the Dean’s and Director’s interest lists. Mr. Kahlenberg does not remove
consideration of whether an applicant is a recruited athlete in his preferred simulation (explaining that
such an approach “is sometimes perceived as radical”), but I simulate the effect of that change in
order to ensure that I am considering all potentially available race-neutral alternatives.
230. Using my preferred year-by-year model of admissions from Section 5, I simulate how
the elimination of these practices would affect Harvard’s admitted class. My method closely follows
that used by Mr. Kahlenberg in his report. First, I estimate my model of admissions using data on
applicants to the class of 2019.169 Then, I simulate the effect of eliminating consideration of race,
lineage status, athletic-recruit status, whether an applicant is the child of Harvard faculty or staff, and
whether an applicant is on the Dean’s or Director’s interest lists. (I do so by replacing the estimated
coefficient of the relevant variables—e.g., a coefficient estimating the effect of being a lineage
applicant on an applicant’s likelihood of admission—with zero.) I then simulate the class that would
be admitted using each applicant’s predicted probability of admission in this modified model of
admissions, and examine how the composition of the simulated class compares to that of the actual
admitted class. Note that this method also eliminates the practice of deferred admission because it
simulates filling all seats in the entering class with students who apply in a given year.
231. As Exhibit 35 shows, removing consideration of factors that allegedly benefit White
applicants does little to generate racial diversity. The simulated class has more White students and
many more Asian-American students, but markedly fewer African-American, Hispanic, or Other
(AHO) students than Harvard now admits.

169

Simulation results for earlier years are qualitatively similar, and can be found in the backup for the relevant exhibit.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 104

Simulated racial composition of the admitted class, after eliminating the consideration of race,
lineage, athletic-recruit status, whether an applicant’s parents are Harvard faculty and staff, and
the Dean’s and Director’s interest lists

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Sample consists of applicants to the class of 2019 in Prof. Arcidiacono’s corrected expanded sample who are in my preferred year-byyear regression model. Simulation eliminates consideration of race, lineage status, recruited-athlete status, whether an applicant’s parents
are Harvard faculty or staff, whether an applicant is on the Dean’s or Director’s interest list, and the proportion of the applicant’s high
school and neighborhood that is African-American, Hispanic, and White. In addition, recruited athletes are reassigned to rating
combinations in the regression sample that contain the next highest athletic rating.

7.3.3. Increasing the weight placed on socioeconomic factors
232. Mr. Kahlenberg also suggests that Harvard could attain a diverse class without
considering race if it increased its consideration of applicants’ socioeconomic status in the admissions
process. To examine the likely effect of doing so, I again use my preferred admissions model to
conduct a series of simulations. The simulations build on the results presented in the prior section by
considering what would happen if admissions officers at Harvard did not consider race, but gave
greater consideration to various indicators of lower socioeconomic status. To conduct the
simulations, I proceed in several steps.
233. First, I estimate my preferred model of admissions. Then, I remove consideration of race,
lineage status, recruited-athlete status, whether an applicant is the child of Harvard faculty or staff,

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 105

and whether an applicant is on the Dean’s or Director’s interest list.170 Next, I simulate an increased
preference for students who possess various measurable indicators of lower socioeconomic status. I
do this by artificially increasing the probability of admission for applicants who meet one or more of
the following criteria: (1) the admissions officer reading the applicant’s file considered the applicant
to be “disadvantaged,” (2) neither of the applicant’s parents attended college (i.e., the applicant is
considered a first-generation college student), (3) the applicant requested a waiver of the application
fee, or (4) the estimated median family income of students in the applicant’s neighborhood is at or
below $65,000 (Harvard’s threshold for zero parental contribution).171
234. In the simulations, I introduce a low-SES boost that is proportional to the number of the
criteria that an applicant meets. An applicant who meets all four criteria, for example, gets the full
low-SES boost, while an applicant who meets only two criteria gets a boost equal to one-half of the
full boost. I start by setting the full boost at two additional points to an applicant’s admissions index
(i.e., the input into the logit function that determines her probability of admission). This is about half
the size of the boost simulated by Mr. Kahlenberg.172 It is about one-quarter the size of the increase in
the admissions index associated with having exceptional profile rating combinations (those with
admission rates between 80% and 96%), and it is nearly one-third the size of the advantage associated
with having very strong profile ratings combinations (those with admission rates between 54% and
67%).173 An increase in an applicant’s admissions index translates into an increase in her predicted
probability of admission, but the size of the increase to her predicted probability of admission
depends on her initial predicted probability of admission. For example, adding a low-SES boost of 2
points to the admissions index for a candidate with a 1% predicted probability of admission raises her
predicted probability of admission to 7%. Adding a boost of 2 points to the linear prediction for a
candidate with a 50% predicted probability of admission, however, would increase her predicted
probability of admission to 88%.174
235. As noted above, I start by assuming a low-SES boost of 2 for an applicant who possesses
all four of the indicators of low-SES status. If an applicant meets fewer than four of the criteria listed

170

Following Mr. Kahlenberg’s approach, I recode athletes with an athletic rating of 1 to have an athletic rating of 2,
assigning them to the appropriate ratings combination in the regression sample.
171
I exclude from this simulation an indicator of whether the applicant applied for financial aid, because three-quarters of
all applicants apply for aid, rendering it a poor proxy for socioeconomic disadvantage. The estimated median family
income figures come from data acquired from the College Board and made available to SFFA and its experts. In these
data, an applicant’s neighborhood is determined based on the applicant’s address. A neighborhood is defined by the
College Board and consists of one or more contiguous census tracts.
172
Mr. Kahlenberg simulates a preference that is half the size of the athletic recruit coefficient in Prof. Arcidiacono’s
model.
173
These two sets of rating combinations have the highest admission rates across all rating combinations in my regression
sample. See workpaper.
174
See workpaper.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 106

above, her baseline low-SES boost is lower. If she meets three of the criteria above, she receives a
boost of 1.5; if she meets two of the criteria above, she receives a boost of 1; if she meets one of the
criteria above, she receives a boost of 0.5.
236. To evaluate the impact of increasing the magnitude of the boost, I then scale up the size
of each applicant’s low-SES boost by a factor of 2 (denoted by 2x), 3 (denoted by 3x), and so on. For
example, in later simulations where I refer to a 2x low-SES boost, I mean that an applicant in that
simulation who satisfies all four low-SES criteria receives a boost of 4 points (doubled from the
baseline of 2 points); an applicant who satisfies three criteria receives a boost of 3 points (doubled
from the baseline of 1.5 points); and so on.
237. My method differs somewhat from Mr. Kahlenberg’s method for simulating increased
weight on socioeconomic factors. In his race-neutral alternative simulations—which examine the
effect of multiple race-neutral practices, not just an increased preference for low-SES applicants—
Mr. Kahlenberg simulates eliminating consideration of whether an applicant has been identified by
admissions officers as disadvantaged, whether the applicant is a first-generation college student,
whether the applicant applied for financial aid, and whether the applicant requested a fee waiver, but
then simulates an increased preference only for students who are identified as disadvantaged. My
approach is more inclusive and flexible, simulating an increased boost for a broader set of students of
lower SES, with the size of the boost for each applicant varying with the number of indicators of low
socioeconomic status that she exhibits.
238. Exhibit 36 illustrates how the racial composition of the admitted class would be expected
to change if Harvard placed varying degrees of additional weight on the low-SES attributes noted
above and eliminated the practices (discussed above) that are alleged to benefit White applicants. The
first two columns in Exhibit 36 report the racial composition of the actual class and the simulated
class in a world where Harvard eliminates consideration of race without undertaking additional raceneutral approaches to increase racial diversity. The third bar shows what would be expected to
happen if, in addition to eliminating consideration of race and factors that allegedly benefit White
applicants, Harvard gave each low-SES applicant an additional boost of the size discussed above in
paragraph 235. The next bar shows what would happen if Harvard doubled the maximum low-SES
boost, and so on.175 Exhibit 37 summarizes the simulated change in the racial composition of the
admitted class under this alternative admissions regime as compared to the actual admitted class of
2019.
239. As noted above, if Harvard were to eliminate consideration of race, the share of AfricanThe full range 1-10x is located in the backup to the exhibit, as are results for the classes of 2014 – 2018, which are
qualitatively similar.
175

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 107

American students in the admitted class would be expected to drop from 14% to 6%. The fraction of
Hispanic students would also fall, while the fraction of Asian-American and White students would
rise. If Harvard then applied a low-SES boost (as described above) and eliminated the practices
alleged to benefit White applicants, the share of African-American students in the admitted class
would be expected to remain constant at 6%—still radically below the current level. The fraction of
Hispanic students would rise, from 9% to 11%, remaining about 20% lower than in the actual class.
The fraction of White admitted students would fall back to a level comparable to the current class,
while the fraction of Asian-American students in the admitted class would grow from 27% to 31%.
Harvard would need to increase the low-SES boost to more than six times the baseline (i.e., to a
maximum factor of 12) in order for the expected proportion of African-American students among
admitted students to approximate the current level. At that point, hundreds of low-SES applicants
would be receiving an incremental boost larger than that given to candidates with the most
exceptional academic, extracurricular, personal, and athletic ratings.176

Increasing the weight placed on socioeconomic characteristics could help generate racial diversity

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Sample consists of applicants to the class of 2019 in Prof. Arcidiacono’s corrected expanded sample who are in my preferred year-byyear regression model. Simulation eliminates preferences for race, lineage status, recruited-athlete status, whether an applicant’s parents
are Harvard faculty and staff, whether the applicant appears on the Dean’s or Director’s interest list, and the proportion of the applicant’s
high school and neighborhood that is African-American, Hispanic, and White. In addition, recruited athletes are reassigned to rating
combinations in the regression sample which contain the next highest athletic rating. Applicants with certain socioeconomic
characteristics are given a low-SES boost by adding a value to their admission index. The value is equal to 0.5 multiplied by a given integer
multiplier, multiplied by the number of characteristics an applicant displays out of the following: disadvantaged, requested a fee waiver,
first generation college student, neighborhood median income less than or equal to $65,000.

176

See workpaper.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 108

Estimated change in racial composition after removing consideration of race, increasing weight on
socioeconomic characteristics, and eliminating the practices alleged to benefit White applicants

Predicted Class Without Consideration of Race and Factors that Allegedly Advantage White
Applicants, Change from Actual Class

Race
1. White

Actual
Admitted
Class

1x
Low-SES
Boost

2x
Low-SES
Boost

3x
Low-SES
Boost

4x
Low-SES
Boost

5x
Low-SES
Boost

6x
Low-SES
Boost

10x
Low-SES
Boost
-236

676

+34

-4

-44

-87

-127

-162

2. Asian-American

402

+120

+118

+113

+106

+98

+90

+71

3. Hispanic or Other

233

-51

-18

+19

+60

+99

+133

+204

4. African-American

234

-130

-112

-92

-71

-51

-34

+4

5. Race Missing

134

+27

+16

+4

-7

-18

-26

-43

Predicted Class Without Consideration of Race and Factors that Allegedly Advantage White
Applicants, % Change from Actual Class

Race
1. White

Actual
Admitted
Class

1x
Low-SES
Boost

2x
Low-SES
Boost

3x
Low-SES
Boost

4x
Low-SES
Boost

5x
Low-SES
Boost

6x
Low-SES
Boost

10x
Low-SES
Boost

40%

+5%

-1%

-7%

-13%

-19%

-24%

-35%

2. Asian-American

24%

+30%

+29%

+28%

+26%

+24%

+22%

+18%

3. Hispanic or Other

14%

-22%

-8%

+8%

+26%

+42%

+57%

+88%

4. African-American

14%

-55%

-48%

-39%

-30%

-22%

-15%

+2%

8%

+20%

+12%

+3%

-6%

-13%

-20%

-32%

5. Race Missing

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Sample consists of applicants to the class of 2019 in Prof. Arcidiacono’s corrected expanded sample who are in my preferred year-byyear regression model. Simulation eliminates consideration of race, lineage status, recruited-athlete status, whether an applicant’s parents
are Harvard faculty and staff, whether the applicant appears on the Dean’s or Director’s interest list, and the proportion of the applicant’s
high school and neighborhood that is African-American, Hispanic, and White. In addition, recruited athletes are reassigned to rating
combinations in the regression sample that contain the next highest athletic rating. Applicants with certain socioeconomic characteristics
are given a low-SES boost by adding a value to their admission index. The value is equal to 0.5 multiplied by a given integer multiplier,
multiplied by the number of characteristics an applicant displays out of the following: disadvantaged, requested a fee waiver, first
generation college student, neighborhood median income less than or equal to $65,000.

240. Increasing the size of the low-SES boost in this manner would also be expected to lead to
changes in the admitted class in other respects, many of which Harvard might well consider
deleterious. For example, if Harvard were to increase the size of the low-SES boost by four or five
times—enough for the combined share of AHO students in the expected class to resemble that of the
current class, but still not enough to restore a comparable share of African-American students177––
and eliminate the practices alleged to benefit White applicants, numerous measures of excellence in
177

If Harvard increased the size of the low-SES preference four times relative to the baseline, that would yield a
preference for students identified as “disadvantaged” that is roughly equivalent to the preference Mr. Kahlenberg gives
them in his simulations. At five times the baseline preference, students identified as “disadvantaged” receive about one
and a half times the preference in my model as in Mr. Kahlenberg’s simulations. In addition, under my more flexible
simulation, students who are first-generation or who receive fee waivers also receive a boost about the same size on
average as the one accruing to applicants identified as “disadvantaged” (see workpaper).
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 109

Harvard’s class would drop substantially. This can be seen in Exhibit 38, which summarizes changes
across a variety of characteristics of the admitted class. For example, the fraction of admitted students
receiving an academic rating of 1 or 2 would be expected to drop by an amount between 13% and
22%. The fraction of students receiving top extracurricular and personal ratings would also fall, and
the fraction with top athletic ratings would be cut by a third. In addition, as the magnitude of the lowSES boost increases to 3x the baseline boost and beyond, the fraction of admitted students who are
Asian-American begins to fall, rather than rise.
241. The admitted class would also be expected to look markedly different in other
dimensions. The fraction of students intending to concentrate in the humanities and social sciences
would be expected to fall, while the fraction intending to concentrate in biological sciences would be
expected to rise. The fraction of admitted students who are children of Harvard and Radcliffe alumni
would fall, as would the number of admitted students who are children of Harvard faculty and staff.
The number of athletic recruits would drop by half.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 110

Increasing the weight placed on socioeconomic characteristics would be expected to markedly alter
the characteristics of Harvard’s admitted class
Predicted Class Without Consideration of Race and Factors that
Allegedly Advantage White Applicants
3x Low-SES Boost

4x Low-SES Boost

5x Low-SES Boost

Actual
Admitted
Class
Outcome Measures
1.
2.
3.
4.
5.

Race
White
Asian-American
Hispanic or Other
African-American
Race Missing

6.
7.
8.
9.

Academic
Average Composite SAT Score
Average Composite ACT Score
Average Converted GPA
Average Academic Index

10.
11.
12.
13.

Predicted
Value

% Change

Predicted
Value

% Change

Predicted
Value

% Change

[A]

[B]

([B]-[A])/[A]

[C]

([C]-[A])/[A]

[D]

([D]-[A])/[A]

676
402
233
234
134

632
515
252
142
138

-7%
+28%
+8%
-39%
+3%

589
508
293
163
127

-13%
+26%
+26%
-30%
-6%

549
500
332
183
116

-19%
+24%
+42%
-22%
-13%

2244
33.1
77.0
228

2213
33.0
77.2
227

-1%
-0.5%
+0.3%
-0.4%

2189
32.7
77.1
225

-2%
-1%
+0.1%
-1%

2164
32.4
76.9
224

-4%
-2%
-0.1%
-2%

Fraction with Profile Rating of 1 or 2
Academic
76%
Extracurricular
62%
Personal
71%
Athletic
27%

72%
61%
68%
19%

-5%
-2%
-5%
-30%

66%
57%
64%
18%

-13%
-9%
-11%
-33%

59%
52%
59%
17%

-22%
-17%
-17%
-38%

259

104

-60%

86

-67%

68

-74%

72

24

-67%

19

-73%

15

-79%

180

89

-51%

88

-51%

88

-51%

44

20

-54%

17

-61%

13

-69%

839

848

+1%

851

+1%

855

+2%

Applicant Characteristics
14. Number of Lineage Students
Number of Double Lineage
15.
Students
16. Number of Recruited Athletes
17.

Number of Children of Harvard
Faculty and Staff

18.

Number of Students on Dean’s
and Director’s Interest Lists

19. Number of Female Students

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 111

20.
21.
22.
23.
24.
25.
26.
27.

Concentration
Social Sciences
Humanities
Biological Sciences
Physical Science
Engineering
Computer Science
Mathematics
Unspecified

25%
15%
21%
7%
13%
6%
6%
7%

24%
14%
23%
8%
13%
6%
7%
6%

-4%
-7%
+8%
+8%
+4%
-4%
+5%
-12%

24%
13%
23%
8%
13%
6%
7%
6%

-5%
-9%
+11%
+6%
+5%
-7%
+3%
-9%

23%
13%
24%
8%
14%
6%
6%
6%

-7%
-12%
+15%
+3%
+8%
-9%
+2%
-6%

28.
29.
30.
31.
32.

Geography
Number Rural
Number in Northeast
Number in Midwest
Number in South
Number in West

59
694
207
379
399

80
627
221
395
437

+35%
-10%
+7%
+4%
+10%

87
604
217
407
451

+48%
-13%
+5%
+7%
+13%

94
582
214
419
464

+59%
-16%
+3%
+11%
+16%

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Sample consists of applicants to the class of 2019 in Prof. Arcidiacono’s corrected expanded sample who are in my preferred year-byyear regression model. Simulation eliminates consideration of race, lineage status, recruited-athlete status, whether an applicant’s parents
are Harvard faculty and staff, whether the applicant appears on the Dean’s or Director’s interest list, and the proportion of the applicant’s
high school and neighborhood that is African-American, Hispanic, and White. In addition, recruited athletes are reassigned to rating
combinations in the regression sample that contain the next highest athletic rating. Applicants with certain socioeconomic characteristics
are given a low-SES boost by adding a value to their admission index. The value is equal to 0.5 multiplied by a given integer multiplier,
multiplied by the number of characteristics an applicant displays out of the following: disadvantaged, requested a fee waiver, first
generation college student, neighborhood median income less than or equal to $65,000.

242. Additionally, raising the low-SES boost four or five times relative to the baseline would
likely increase the financial need of the accepted class, as evidenced by Exhibit 39.178 Approximately
27–32% more admitted students (or about 294–356 additional students) would be expected to apply
for financial aid under this alternative regime. As of 2017, Harvard’s average financial aid grant was
about $50,000 per student per year. Given this estimate, this alternative regime could necessitate an
increase in Harvard’s financial spending by roughly $59–71 million per year (assuming all four
classes at Harvard at a given time receive equivalent aid), relative to the current annual aid budget of
about $170 million.179

178

The increase would be particularly notable among AHO applicants, the majority of whom would now be flagged as
disadvantaged under the simulation. See workpaper.
179
Harvard University, “Harvard at a Glance,” available at https://www.harvard.edu/about-harvard/harvard-glance,
accessed December 14, 2017 (“More than 55 percent of Harvard College students receive scholarship aid, and the average
grant this year is $50,000. Since 2007, Harvard’s investment in financial aid has climbed by more than 75 percent, from
$96.6 million to $170 million per year.”). See workpaper.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 112

Increasing the weight placed on socioeconomic characteristics would likely increase the financial
need of Harvard’s admitted class
Predicted Class Without Consideration of Race and Factors that Allegedly
Advantage White Applicants
3x Low-SES Boost

Socioeconomic Status
1. Number First Generation College

Actual
Admitted
Class
[A]

Predicted
Value
[B]

% Change
([B]-[A])/[A]
+185%

4x Low-SES Boost
Predicted
Value
[C]

Predicted
Value
[D]
508

% Change
([D]-[A])/[A]
+323%

120

342

2. Number Disadvantaged

297

714

+140%

875

+195%

1024

+245%

3. Number with Fee Waiver

309

718

+132%

880

+185%

1031

+234%

1102

1328

+21%

1396

+27%

1458

+32%

4. Number with Financial Aid

428

% Change
([C]-[A])/[A]
+257%

5x Low-SES Boost

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Notes: Sample consists of applicants to the class of 2019 in Prof. Arcidiacono’s corrected expanded sample who are in my preferred yearby-year regression model. Simulation eliminates consideration of race, lineage status, recruited-athlete status, whether an applicant’s
parents are Harvard faculty and staff, whether the applicant appears on the Dean’s or Director’s interest list, and the proportion of the
applicant’s high school and neighborhood that is African-American, Hispanic, and White. In addition, recruited athletes are reassigned to
rating combinations in the regression sample that contain the next highest athletic rating. Applicants with certain socioeconomic
characteristics are given a low-SES boost by adding a value to their admission index. The value is equal to 0.5 multiplied by a given integer
multiplier, multiplied by the number of characteristics an applicant displays out of the following: disadvantaged, requested a fee waiver,
first generation college student, neighborhood median income less than or equal to $65,000.

243. Taken together, this evidence suggests that Harvard could achieve a student body
comparably diverse in race and ethnicity by placing greater emphasis on socioeconomic factors in the
admissions process—but only with significant changes to a variety of characteristics of the admitted
class, including lower profile ratings on all four dimensions (academic, extracurricular, athletic, and
personal). These results are consistent with the academic literature, which indicates that using
socioeconomic preferences in an effort to increase racial diversity necessarily diminishes the ability
to select for applicant quality in other dimensions, relative to considering race.
7.3.4. Eliminating additional admissions policies that allegedly advantage White applicants
244. Mr. Kahlenberg also suggests that Harvard could attain racial diversity without
considering race by eliminating any consideration of whether an applicant’s family has donated or
has the capacity to donate to Harvard.180 Other commentators have suggested that eliminating
consideration of standardized test scores could increase racial diversity. In this section, I consider
whether eliminating any of these practices would create racial diversity if Harvard did not consider
race. I also consider whether using these policies in combination with all of the policies considered
above would generate a class comparable in diversity to Harvard’s current admitted class.

180

Kahlenberg Report, pp. 33–36.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 113

7.3.4.1. Eliminate any consideration of whether an applicant’s family could donate or has donated to
Harvard
245. Mr. Kahlenberg alleges that Harvard’s admissions process considers as a positive factor
the propensity of an applicant’s family to donate to the university, and asserts that eliminating this
alleged practice could increase racial diversity.181 I do not have data on any donations made to
Harvard by the family members of applicants, so I cannot test this hypothesis directly.
182

And, as I have shown above,
removing consideration of those two lists does not help improve racial diversity.
7.3.4.2. Reducing or eliminating consideration of standardized test scores
246. Some commentators have proposed that universities could increase racial diversity by
reducing or eliminating reliance on standardized tests like the SAT and ACT. Such arguments rely on
the theory that SAT and ACT scores are correlated with socioeconomic status, because scores can be
improved by parental and school investments (e.g., SAT preparation courses), and therefore that
consideration of standardized test scores could conceivably have the effect of biasing admission
decisions against AHO applicants (who are more likely to be disadvantaged).183
247. Building on the simulation I conduct in 7.3.3, I simulate what would happen if Harvard
eliminated any consideration given to the SAT (SAT I), SAT Subject Tests (SAT II), ACT, and the
Academic Index (which includes standardized test scores as a component). I find that if Harvard
eliminated its consideration of race and factors that Mr. Kahlenberg alleges advantage White
applicants, while also giving low-SES applicants the low-SES boost discussed above, further
eliminating consideration of standardized test scores would not generate a student body comparable
in diversity to Harvard’s current class, and would also decrease the quality of the admitted class in
several respects.
248. As shown by Exhibit 40 and Exhibit 41, these practices—even taken together—are
unable to produce a comparably diverse class without placing very heavy weight on low-SES status.
Generating a class with AHO representation comparable to that of the current class would require

181

Kahlenberg Report, pp. 31, 33–34.
Kahlenberg Report, pp. 33–36.
183
Saul Geiser and Maria Veronica Santelices, “Validity of High-School Grades in Predicting Student Success beyond the
Freshman Year: High-School Record vs. Standardized Tests as Indicators of Four-Year College Outcomes,” Research &
Occasional Paper Series CSHE 6.07, 2007, pp. 1–35, at pp. 1–2 and 24; Thomas Espenshade and Chang Chung,
“Diversity Outcomes of Test-Optional Policies,” SAT Wars, The Case for Test-Optional Admissions, ed. Joseph A. Soares
(Teachers College Press, 2011), pp. 177–200, at p. 190.
182

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 114

raising the low-SES boost to three times the baseline value. And that would come at a cost (Exhibit
42). A class with a share of AHO students comparable to that of the current class (shown in the 3x
bar) would exhibit a 17% decline in the fraction of applicants with top academic ratings, as well as
drops in the fraction of students with top extracurricular and personal ratings. The fraction of AsianAmerican students would also start to decline slightly as more weight is placed on low-SES status.
Athletic ratings would become worse, and the number of athletic recruits would drop, as would the
representation of lineage applicants. In addition, the financial need of the class would increase a great
deal, generating substantial costs to Harvard (Exhibit 43). Generating an admitted class with number
of African-American students comparable to that of the current admitted class (the 6x bar below)
would come with even greater costs in these dimensions, including double-digit drops in the fraction
of applicants with top ratings across all four profile ratings.

Eliminating consideration of standardized test scores in conjunction with other race-neutral
policies: Racial composition

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note Sample consists of applicants to the class of 2019 in Prof. Arcidiacono’s corrected expanded sample who are in my preferred year-byyear regression model. Simulation eliminates consideration of race, lineage status, recruited-athlete status, whether an applicant’s parents
are Harvard faculty and staff, whether the applicant appears on the Dean’s or Director’s interest list, standardized test scores, and the
proportion of the applicant’s high school and neighborhood that is African-American, Hispanic, and White. In addition, recruited athletes
are reassigned to rating combinations in the regression sample that contain the next highest athletic rating. Applicants with certain
socioeconomic characteristics are given a low-SES boost by adding a value to their admission index. The value is equal to 0.5 multiplied by
a given integer multiplier, multiplied by the number of characteristics an applicant displays out of the following: disadvantaged, requested
a fee waiver, first generation college student, neighborhood median income less than or equal to $65,000.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 115

Eliminating consideration of standardized test scores in conjunction with other race-neutral
policies: Changes in racial composition

Predicted Class Without Consideration of Race and Factors that Allegedly Advantage White
Applicants, Change from Actual Class

Race
1. White

Actual
Admitted
Class

1x
Low-SES
Boost

2x
Low-SES
Boost

3x
Low-SES
Boost

4x
Low-SES
Boost

5x
Low-SES
Boost

6x
Low-SES
Boost

10x
Low-SES
Boost
-252

676

+25

-22

-70

-117

-158

-190

2. Asian-American

402

+77

+74

+69

+63

+58

+53

+47

3. Hispanic or Other

233

-13

+26

+68

+110

+147

+176

+229

4. African-American

234

-104

-82

-58

-36

-17

-2

+23

5. Race Missing

134

+16

+4

-9

-20

-29

-36

-47

Predicted Class Without Consideration of Race and Factors that Allegedly Advantage White
Applicants, % Change from Actual Class

Race
1. White

Actual
Admitted
Class

1x
Low-SES
Boost

2x
Low-SES
Boost

3x
Low-SES
Boost

4x
Low-SES
Boost

5x
Low-SES
Boost

6x
Low-SES
Boost

10x
Low-SES
Boost

40%

+4%

-3%

-10%

-17%

-23%

-28%

-37%

2. Asian-American

24%

+19%

+18%

+17%

+16%

+14%

+13%

+12%

3. Hispanic or Other

14%

-6%

+11%

+29%

+47%

+63%

+76%

+98%

4. African-American

14%

-45%

-35%

-25%

-15%

-7%

-1%

+10%

8%

+12%

+3%

-6%

-15%

-22%

-27%

-35%

5. Race Missing

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Sample consists of applicants to the class of 2019 in Prof. Arcidiacono’s corrected expanded sample who are in my preferred year-byyear regression model. Simulation eliminates consideration of race, lineage status, recruited-athlete status, whether an applicant’s parents
are Harvard faculty and staff, whether the applicant appears on the Dean’s or Director’s interest list, standardized test scores, and the
proportion of the applicant’s high school and neighborhood that is African-American, Hispanic, and White. In addition, recruited athletes
are reassigned to rating combinations in the regression sample that contain the next highest athletic rating. Applicants with certain
socioeconomic characteristics are given a low-SES boost by adding a value to their admission index. The value is equal to 0.5 multiplied by
a given integer multiplier, multiplied by the number of characteristics an applicant displays out of the following: disadvantaged, requested
a fee waiver, first generation college student, neighborhood median income less than or equal to $65,000.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 116

Eliminating deferred admission and consideration of standardized test scores in conjunction with
other race-neutral policies: Changes in class quality

Predicted Class Without Consideration of Race and Factors that
Allegedly Advantage White Applicants
2x Low-SES Boost

3x Low-SES Boost

4x Low-SES Boost

Actual
Admitted
Class
Outcome Measures
1.
2.
3.
4.
5.

Race
White
Asian-American
Hispanic or Other
African-American
Race Missing

6.
7.
8.
9.

Academic
Average Composite SAT Score
Average Composite ACT Score
Average Converted GPA
Average Academic Index

10.
11.
12.
13.

% Change

Predicted
Value

% Change

Predicted
Value

% Change

[A]

[B]

([B]-[A])/[A]

[C]

([C]-[A])/[A]

[D]

([D]-[A])/[A]

676
402
233
234
134

654
476
259
152
138

-3%
+18%
+11%
-35%
+3%

606
471
301
176
125

-10%
+17%
+29%
-25%
-6%

559
465
343
198
114

-17%
+16%
+47%
-15%
-15%

2244
33.1
77.0
228

2198
32.7
76.8
225

-2%
-1%
-0.2%
-1%

2172
32.4
76.6
224

-3%
-2%
-0.4%
-2%

2145
32.1
76.5
222

-4%
-3%
-1%
-3%

Fraction with Profile Rating of 1 or 2
Academic
76%
Extracurricular
62%
Personal
71%
Athletic
27%

69%
62%
70%
20%

-9%
+0.3%
-1%
-23%

63%
58%
66%
19%

-17%
-7%
-7%
-27%

56%
53%
61%
18%

-26%
-15%
-14%
-31%

259

112

-57%

93

-64%

73

-72%

72

25

-65%

20

-72%

16

-78%

180

93

-48%

92

-49%

91

-49%

44

22

-51%

18

-59%

14

-67%

839

866

+3%

869

+4%

872

+4%

Applicant Characteristics
14. Number of Lineage Students
Number of Double Lineage
15.
Students
16. Number of Recruited Athletes
Number of Children of Harvard
17.
Faculty and Staff
18.

Predicted
Value

Number of Students on Dean’s
and Director’s Interest Lists

19. Number of Female Students

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 117

20.
21.
22.
23.
24.
25.
26.
27.

Concentration
Social Sciences
Humanities
Biological Sciences
Physical Science
Engineering
Computer Science
Mathematics
Unspecified

25%
15%
21%
7%
13%
6%
6%
7%

24%
14%
23%
8%
13%
6%
7%
6%

-2%
-3%
+7%
+2%
+1%
-8%
+2%
-8%

24%
14%
24%
7%
13%
6%
6%
6%

-3%
-6%
+11%
+0.03%
+2%
-11%
+1%
-5%

23%
13%
25%
7%
13%
5%
6%
7%

-6%
-10%
+16%
-2%
+5%
-12%
-0.3%
-3%

28.
29.
30.
31.
32.

Geography
Number Rural
Number in Northeast
Number in Midwest
Number in South
Number in West

59
694
207
379
399

77
646
224
379
431

+30%
-7%
+8%
-0.1%
+8%

85
622
219
390
448

+43%
-10%
+6%
+3%
+12%

92
597
216
403
463

+55%
-14%
+4%
+6%
+16%

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Sample consists of applicants to the class of 2019 in Prof. Arcidiacono’s corrected expanded sample who are in my preferred year-byyear regression model. Simulation eliminates consideration of race, lineage status, recruited-athlete status, whether an applicant’s parents
are Harvard faculty and staff, whether the applicant appears on the Dean’s or Director’s interest list, standardized test scores, and the
proportion of the applicant’s high school and neighborhood that is African-American, Hispanic, and White. In addition, recruited athletes
are reassigned to rating combinations in the regression sample that contain the next highest athletic rating. Applicants with certain
socioeconomic characteristics are given a low-SES boost by adding a value to their admission index. The value is equal to 0.5 multiplied by
a given integer multiplier, multiplied by the number of characteristics an applicant displays out of the following: disadvantaged, requested
a fee waiver, first generation college student, neighborhood median income less than or equal to $65,000.

Eliminating deferred admission and consideration of standardized test scores in conjunction with
other race-neutral policies: Changes in financial need
Predicted Class Without Consideration of Race and Factors that Allegedly
Advantage White Applicants
2x Low-SES Boost

Socioeconomic Status
1. Number First Generation College

Actual
Admitted
Class
[A]

Predicted
Value
[B]

% Change
([B]-[A])/[A]
+152%

3x Low-SES Boost
Predicted
Value
[C]

Predicted
Value
[D]
483

% Change
([D]-[A])/[A]
+303%

120

303

2. Number Disadvantaged

297

649

+118%

820

+176%

981

+230%

3. Number with Fee Waiver

309

662

+114%

835

+170%

1001

+224%

1102

1306

+19%

1378

+25%

1445

+31%

4. Number with Financial Aid

395

% Change
([C]-[A])/[A]
+229%

4x Low-SES Boost

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Sample consists of applicants to the class of 2019 in Prof. Arcidiacono’s corrected expanded sample who are in my preferred year-byyear regression model. Simulation eliminates consideration of race, lineage status, recruited-athlete status, whether an applicant’s parents
are Harvard faculty and staff, whether the applicant appears on the Dean’s or Director’s interest list, standardized test scores, and the
proportion of the applicant’s high school and neighborhood that is African-American, Hispanic, and White. In addition, recruited athletes
are reassigned to rating combinations in the regression sample that contain the next highest athletic rating. Applicants with certain
socioeconomic characteristics are given a low-SES boost by adding a value to their admission index. The value is equal to 0.5 multiplied by
a given integer multiplier, multiplied by the number of characteristics an applicant displays out of the following: disadvantaged, requested
a fee waiver, first generation college student, neighborhood median income less than or equal to $65,000.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 118

7.3.5. Expanding policies that may increase racial diversity
249. In this section I consider how the racial composition and quality of the admitted class
would likely change if Harvard were to further expand transfer admissions, outreach, and recruiting
efforts. First, I discuss these practices individually. Then, I employ a simulation to demonstrate that
even if Harvard were able to double the number of applicants flagged as disadvantaged through
outreach and recruiting efforts—which is unlikely—these practices, in addition to all the other raceneutral alternatives discussed above, would not produce a class comparable in diversity to the one
Harvard currently admits.
7.3.5.1. Increasing transfer admissions
250. Mr. Kahlenberg suggests that recruiting and accepting more transfer applicants—
particularly from community colleges—would increase racial diversity because transfer students are
more racially diverse than applicants to the freshman class. I do not have data on the pool of potential
transfer applicants from community colleges, so I cannot simulate the impact of a large preference for
such students on class composition and quality. Data on current transfer applicants, however, suggest
that such a policy is not likely to be effective.
251. First, it is important to note that Harvard rarely admits transfer students. For example,
only 17 transfer applicants were accepted during the admissions cycle for the freshman class of
2019.184 For Harvard to admit a significantly greater number of transfer students, as Mr. Kahlenberg
proposes, would be a dramatic change. Given how few students drop out of Harvard, a sizable
increase in the number of transfer students would require Harvard to reserve spots for transfer
students by admitting a substantially smaller freshman class.185
252. Second, the current pool of transfer applicants is not more diverse than the regular
applicant pool. If anything, transfer applicants are less likely than regular applicants to be AHO.
Furthermore, the current pool of transfer applicants has lower academic credentials and ratings (on
average) than the freshman applicant pool.186 Taken together, these facts suggest that increasing the
number of transfer students is not likely to increase diversity while preserving class quality.

184

See workpaper.
Four- and six-year graduation rates by race, HARV00003906; Harvard College, “What is Harvard’s graduation rate?,”
available at https://college.harvard.edu/what-harvards-graduation-rate, accessed December 5, 2017 (“The College’s
graduation rate is normally 98 percent”).
186
See workpaper.
185

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 119

7.3.5.2. Increasing outreach and recruiting
253. SFFA and Mr. Kahlenberg identify increased outreach and recruiting as a tool to increase
racial diversity. SFFA asserts, in particular, that Harvard could increase racial diversity by
(1) sponsoring campus visits and outreach programs targeting underrepresented high schools,
(2) sending brochures to minority applicants, and (3) recruiting more heavily in disadvantaged
geographic regions.187 Mr. Kahlenberg argues that Harvard does a “poor job” recruiting firstgeneration students and students from economically disadvantaged geographic areas.188
254. Mr. Kahlenberg cites research by Caroline Hoxby and Christopher Avery indicating that
recruiting and informational outreach are crucial to attracting top talent from underrepresented
communities.189 Based on the materials I have reviewed, I understand that Harvard embraces that
idea. None of the findings cited by Mr. Kahlenberg suggests that Harvard could markedly increase
the racial diversity of its admitted class by engaging in further outreach, because Harvard already
well understands the need to engage in outreach, and already engages in extensive efforts on this
front.190
255. For example, Harvard’s Undergraduate Minority Recruitment Program (UMRP)
endeavors to spread awareness about Harvard’s diverse campus community, and the Harvard
application process, among middle- and high school students. Among other initiatives, the UMRP
“sends targeted mailings to many potential applicants of different racial and ethnic backgrounds,
coordinates robust online and social media communications, sends staff to schools and events around
the world, and enlists current students to talk with potential students (both at Harvard and during
hometown recruiting trips, in which enrolled Harvard students travel to their hometowns on behalf of

187

Complaint, pp. 78–81
Kahlenberg Report, pp. 39–40.
189
Kahlenberg Report, pp. 14–15.
190
Email from Jeff A. Neal to William Fitzsimmons, “Draft Gazette Article with Tuition/Smith,” March 24, 2014,
HARV00027590 – 97 at HARV00027594 (“Recruitment begins each year with direct outreach to promising juniors. …
‘Recruitment has provided the foundation for Harvard’s pursuit of excellence for many decades’…‘Members of the
Undergraduate Minority Recruitment Program (UMRP) and the Harvard Financial: Aid Initiative (HFAI) played a key
role in attracting this year’s students’…Members of both organizations telephoned and sent email messages and letters to
prospective applicants. They also conducted recruitment trips around the country and met with middle school and high
school student groups who visited Harvard.”).
188

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 120

Harvard to conduct outreach to potential students in the area).”191
256. The Harvard First Generation Program (HFGP) engages in outreach to students who
would be the first members of their families to attend a four-year college. “The First Generation
program includes a dedicated recruitment program accessible through Harvard’s website,
promotional materials, and the ability to correspond directly with current First Generation students
attending Harvard.”192
257. Harvard also engages in a variety of other outreach efforts. For example, the Harvard
College Connection (HCC) uses social media to reach out to promising students and encourage them
to apply to Harvard.193 Harvard’s Project Teach connects Harvard with seventh-grade students in the
Cambridge public schools, providing classroom materials for teachers, programs for students, and
tools for families to facilitate conversations about college preparation. The Crimson Summer
Academy brings together local public school students for summer classes, field trips, and college
preparatory activities. Similarly, the Cambridge-Harvard Summer Academy (CHSA) allows high
school students to participate in summer enrichment from teachers affiliated with the Harvard
Graduate School of Education. Harvard maintains a close partnership with local public school
Cambridge Rindge and Latin School, engaging its students in tutoring, free summer school courses,
and scholarships to the Harvard Extension School.194
258. Harvard also purchases “search lists” from testing agencies to aid in identifying talented
minority and rural applicants.195 In 2013, the search lists included more than 30,000 SAT, ACT, and
AP test takers who were AHO, as well as about 44,000 AHO applicants with PSAT scores greater

191

Harvard’s Objections and Responses to Plaintiff’s Second Set of Interrogatories, July 20, 2017 (“Second Interrogatory
Response”), p. 15.
192
Second Interrogatory Response, pp. 14–15.
193
Harvard College, “Harvard College Connection,” available at https://college.harvard.edu/admissions/hear-ourstudents/harvard-college-connection, accessed December 12, 2017.
194
Harvard University, “Harvard in the Community,” 2016, available at
https://hwpi.harvard.edu/files/comm/files/2016_cambridge_impact_mailing.pdf, accessed November 27, 2017;
Cambridge Rindge and Latin School, “Support & Enrichment Programs,” available at
http://crls.cpsd.us/academics/support___enrichment_programs, accessed November 27, 2017.
195
Email from Jeff A. Neal to William Fitzsimmons, “Draft Gazette Article with Tuition/Smith,” March 24, 2014,
HARV00027590 – 97 at HARV00027594 (“More than 63 percent of all admitted students and 81 percent of admitted
minority students (including 90 percent of Latinos and 83 percent of African Americans) appeared on the original College
Board and ACT search lists that helped launch Harvard’s outreach program for the Class of 2018.”); McGrath Deposition
2015 at p. 11; Yong Deposition at pp. 264–267; Tables, “Class of 2017 – EA Applicants,” HARV00007766 – 7771;
Table, “Searches 2013 – Class of 2018,” HARV0023564.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 121

than 1100. Harvard used the PSAT, SAT, ACT, and AP search lists to send more than 100,000 letters
to potential applicants to the class of 2018. Harvard casts a wide net when trying to attract applicants
from diverse backgrounds.
259. Furthermore, admissions officers travel the country—and the world—seeking out top
talent. “Harvard sends representatives, including admissions officers and alumni, to conduct
numerous recruitment events throughout the United States, including events targeting students at
secondary schools that do not frequently send students to Harvard.”197
196

260. Mr. Kahlenberg attempts to simulate the potential effects on racial diversity of improved
recruiting efforts. In several of his simulations, he assumes that Harvard could double the number of
applicants who are identified as disadvantaged by Harvard’s admissions officers. He does this by
duplicating the records for all disadvantaged applicants, implicitly assuming that the quality of newly
recruited disadvantaged students would be the same as that of students who already apply. I find it
unlikely (given the current depth and breadth of Harvard’s recruiting efforts) that increased recruiting
would produce such an influx of disadvantaged applicants,198 and that those applicants would be as
qualified as current applicants.199 Mr. Kahlenberg has provided no evidence that this would be
possible or likely.
261. I nevertheless consider how such a response might affect Harvard’s admitted class.
Following Mr. Kahlenberg’s lead, I repeat the simulation outlined in Section 7.3.3, this time
artificially doubling the number of disadvantaged students in the applicant pool by duplicating all
196
Email from Jeff A. Neal to William Fitzsimmons, “Draft Gazette Article with Tuition/Smith,” March 24, 2014,
HARV00027590 – 7 at HARV00027594 (“Last year, Harvard admissions officers visited all 50 states, Puerto Rico,
Jamaica, and Mexico, where we saw nearly 50,000 high school students and parents and met with more than 3,000 high
school guidance counselors” and “Staff members will visit 125 cities this spring and fall in tandem with Duke University,
Georgetown University, the University of Pennsylvania, and Stanford University, targeting high school juniors who may
eventually join the Class of 2019.”).
197
Second Interrogatory Response, p. 14.
198
Researchers suggest that there is a pool of talented low-income students who do not apply to selective institutions, but
the same researchers also note the difficulty of reaching these students, let alone doubling the number of such applicants.
These students—dubbed “the missing one-offs” by economists Caroline Hoxby and Christopher Avery—are often
“isolated from other high achievers, both in terms of geography and in terms of the high schools they attend,” making
recruitment efforts challenging. Notably, the authors suggest that two possible interventions consist of tapping into
alumni networks to reach students at a wide array of high schools, and targeted informational interventions through mail,
online, and social media—types of policies Harvard already employs. Caroline Hoxby and Christopher Avery, “The
Missing “One-Offs”: The Hidden Supply of High-Achieving, Low-Income Students,” Brookings Papers on Economic
Activity, vol. 2013(1), pp. 1–65 at p. 2 and 45.
199
If one assumes that a disadvantaged student is more likely to apply to Harvard the more exceptional her credentials,
then additional disadvantaged applicants recruited into the pool may be less qualified, on average, than current applicants.
In that sense, assuming no loss to quality as the applicant pool expands is extremely conservative. Harvard’s current
disadvantaged applicants are a highly selected group; for example, their SAT scores are well above average for lowincome students in general (see workpaper).

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 122

disadvantaged applicants. I find that even if Harvard’s recruiting efforts were to double the number of
applicants flagged as disadvantaged (and even if those new applicants were of the same quality as
current applicants who are disadvantaged), the set of simulated practices would not produce a class
both as diverse and as high-quality as the one Harvard currently admits.
262. Under the unrealistic assumption that Harvard could actually double the size of its pool
of disadvantaged applicants through increased recruiting, it would need to give less of an advantage
to low-SES applicants than in prior simulations in order to obtain a proportion of AHO students
comparable to that of the current student body (Exhibit 44 and Exhibit 45). But even taken together,
all of the race-neutral strategies would fail to generate a proportion of African-American students
comparable to that of the current class without a very significant preference for low-SES students.
263. A class with a share of AHO students comparable to that of the current class (the 2x bar
in Exhibit 44) would exhibit an expected 17% decline in the fraction of students with top academic
ratings, as well as declines in average SAT and ACT scores and a large decline in top-rated athletes.
Attaining a proportion of African-American students comparable to that of the current class (the 5x
bar in Exhibit 44) would be associated with a decline in average SAT scores, ACT scores, and GPAs,
and more than a 26% decline in the fraction of applicants with academic ratings of 1 or 2. It would
also be associated with a decline in the fraction of applicants with top extracurricular and personal
ratings. The number of athletic recruits would plummet, as would the representation of lineage
students (Exhibit 46). In addition, as in previous simulations, the financial need of the class would
increase a great deal, potentially generating substantial costs to Harvard (Exhibit 47).

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 123

Doubling the number of disadvantaged applicants in conjunction with other race-neutral policies:
Racial composition

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Sample consists of applicants to the class of 2019 in Prof. Arcidiacono’s corrected expanded sample who are in my preferred year-byyear regression model. Simulation eliminates consideration of race, lineage status, recruited-athlete status, whether an applicant’s parents
are Harvard faculty and staff, whether the applicant appears on the Dean’s or Director’s interest list, standardized test scores, and the
proportion of the applicant’s high school and neighborhood that is African-American, Hispanic, and White. In addition, recruited athletes
are reassigned to rating combinations in the regression sample that contain the next highest athletic rating. Applicants with certain
socioeconomic characteristics are given a low-SES boost by adding a value to their admission index. The value is equal to 0.5 multiplied by
a given integer multiplier, multiplied by the number of characteristics an applicant displays out of the following: disadvantaged, requested
a fee waiver, first generation college student, neighborhood median income less than or equal to $65,000. Further, the number of
disadvantaged applicants is doubled.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 124

Doubling the number of disadvantaged applicants in conjunction with other race-neutral policies:
Changes in racial composition
Predicted Class Without Consideration of Race and Factors that Allegedly Advantage White
Applicants, Change from Actual Class

Race
1. White

Actual
Admitted
Class

1x
Low-SES
Boost

2x
Low-SES
Boost

3x
Low-SES
Boost

4x
Low-SES
Boost

5x
Low-SES
Boost

6x
Low-SES
Boost

10x
Low-SES
Boost
-295

676

-35

-97

-154

-201

-236

-259

2. Asian-American

402

+79

+80

+78

+74

+69

+65

+55

3. Hispanic or Other

233

+33

+83

+131

+173

+206

+230

+276

4. African-American

234

-82

-56

-32

-13

-0

+8

+18

5. Race Missing

134

+4

-9

-22

-32

-39

-44

-53

Predicted Class Without Consideration of Race and Factors that Allegedly Advantage White
Applicants, % Change from Actual Class

Race
1. White

Actual
Admitted
Class

1x
Low-SES
Boost

2x
Low-SES
Boost

3x
Low-SES
Boost

4x
Low-SES
Boost

5x
Low-SES
Boost

6x
Low-SES
Boost

10x
Low-SES
Boost

40%

-5%

-14%

-23%

-30%

-35%

-38%

-44%

2. Asian-American

24%

+20%

+20%

+19%

+18%

+17%

+16%

+14%

3. Hispanic or Other

14%

+14%

+36%

+56%

+74%

+88%

+99%

+118%

4. African-American

14%

-35%

-24%

-14%

-6%

-0%

+3%

+7%

8%

+3%

-7%

-16%

-24%

-29%

-33%

-40%

5. Race Missing

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Sample consists of applicants to the class of 2019 in Prof. Arcidiacono’s corrected expanded sample who are in my preferred year-byyear regression model. Simulation eliminates consideration of race, lineage status, recruited-athlete status, whether an applicant’s parents
are Harvard faculty and staff, whether the applicant appears on the Dean’s or Director’s interest list, standardized test scores, and the
proportion of the applicant’s high school and neighborhood that is African-American, Hispanic, and White. In addition, recruited athletes
are reassigned to rating combinations in the regression sample that contain the next highest athletic rating. Applicants with certain
socioeconomic characteristics are given a low-SES boost by adding a value to their admission index. The value is equal to 0.5 multiplied by
a given integer multiplier, multiplied by the number of characteristics an applicant displays out of the following: disadvantaged, requested
a fee waiver, first generation college student, neighborhood median income less than or equal to $65,000. Further, the number of
disadvantaged applicants is doubled.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 125

Doubling the number of disadvantaged applicants in conjunction with other race-neutral policies:
Changes in class quality
Predicted Class Without Consideration of Race and Factors that
Allegedly Advantage White Applicants
1x Low-SES Boost

2x Low-SES Boost

3x Low-SES Boost

Actual
Admitted
Class
Outcome Measures
1.
2.
3.
4.
5.

Race
White
Asian-American
Hispanic or Other
African-American
Race Missing

6.
7.
8.
9.

Academic
Average Composite SAT Score
Average Composite ACT Score
Average Converted GPA
Average Academic Index

10.
11.
12.
13.

Predicted
Value

% Change

Predicted
Value

% Change

Predicted
Value

% Change

[A]

[B]

([B]-[A])/[A]

[C]

([C]-[A])/[A]

[D]

([D]-[A])/[A]

676
402
233
234
134

641
481
266
152
138

-5%
+20%
+14%
-35%
+3%

579
482
316
178
125

-14%
+20%
+36%
-24%
-7%

522
480
364
202
112

-23%
+19%
+56%
-14%
-16%

2244
33.1
77.0
228

2200
32.8
76.7
226

-2%
-1%
-0.3%
-1%

2171
32.5
76.6
224

-3%
-2%
-0.5%
-2%

2142
32.1
76.4
222

-5%
-3%
-1%
-3%

Fraction with Profile Rating of 1 or 2
Academic
76%
Extracurricular
62%
Personal
71%
Athletic
27%

70%
65%
75%
20%

-7%
+5%
+6%
-24%

63%
62%
73%
19%

-17%
-1%
+3%
-27%

56%
57%
69%
18%

-26%
-9%
-3%
-31%

259

107

-59%

83

-68%

60

-77%

72

24

-67%

18

-75%

13

-82%

180

94

-48%

93

-48%

92

-49%

44

20

-54%

16

-64%

12

-74%

839

865

+3%

869

+4%

871

+4%

Applicant Characteristics
14. Number of Lineage Students
Number of Double Lineage
15.
Students
16. Number of Recruited Athletes
17.

Number of Children of Harvard
Faculty and Staff

18.

Number of Students on Dean’s
and Director’s Interest Lists

19. Number of Female Students

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 126

20.
21.
22.
23.
24.
25.
26.
27.

Concentration
Social Sciences
Humanities
Biological Sciences
Physical Science
Engineering
Computer Science
Mathematics
Unspecified

25%
15%
21%
7%
13%
6%
6%
7%

25%
14%
23%
8%
13%
6%
6%
6%

-0.3%
-3%
+6%
+5%
-0.3%
-8%
-0.5%
-9%

25%
14%
23%
8%
13%
6%
6%
6%

-0.4%
-6%
+10%
+3%
+0.3%
-12%
-2%
-6%

24%
13%
24%
7%
13%
5%
6%
7%

-2%
-10%
+14%
-0.5%
+3%
-14%
-3%
-3%

28.
29.
30.
31.
32.

Geography
Number Rural
Number in Northeast
Number in Midwest
Number in South
Number in West

59
694
207
379
399

74
665
238
362
414

+25%
-4%
+15%
-4%
+4%

81
642
232
371
434

+38%
-8%
+12%
-2%
+9%

90
617
228
382
452

+52%
-11%
+10%
+1%
+13%

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Sample consists of applicants to the class of 2019 in Prof. Arcidiacono’s corrected expanded sample who are in my preferred year-byyear regression model. Simulation eliminates consideration of race, lineage status, recruited-athlete status, whether an applicant’s parents
are Harvard faculty and staff, whether the applicant appears on the Dean’s or Director’s interest list, standardized test scores, and the
proportion of the applicant’s high school and neighborhood that is African-American, Hispanic, and White. In addition, recruited athletes
are reassigned to rating combinations in the regression sample that contain the next highest athletic rating. Applicants with certain
socioeconomic characteristics are given a low-SES boost by adding a value to their admission index. The value is equal to 0.5 multiplied by
a given integer multiplier, multiplied by the number of characteristics an applicant displays out of the following: disadvantaged, requested
a fee waiver, first generation college student, neighborhood median income less than or equal to $65,000. Further, the number of
disadvantaged applicants is doubled.

Doubling the number of disadvantaged applicants in conjunction with other race-neutral policies:
Changes in financial need
Predicted Class Without Consideration of Race and Factors that Allegedly
Advantage White Applicants
1x Low-SES Boost

Socioeconomic Status
1. Number First Generation College

Actual
Admitted
Class
[A]

Predicted
Value
[B]

% Change
([B]-[A])/[A]
+157%

2x Low-SES Boost
Predicted
Value
[C]

Predicted
Value
[D]
542

% Change
([D]-[A])/[A]
+352%

120

309

2. Number Disadvantaged

297

773

+160%

980

+230%

1168

+293%

3. Number with Fee Waiver

309

713

+131%

920

+198%

1110

+259%

1102

1332

+21%

1415

+28%

1489

+35%

4. Number with Financial Aid

426

% Change
([C]-[A])/[A]
+255%

3x Low-SES Boost

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Sample consists of applicants to the class of 2019 in Prof. Arcidiacono’s corrected expanded sample who are in my preferred year-byyear regression model. Simulation eliminates consideration of race, lineage status, recruited-athlete status, whether an applicant’s parents
are Harvard faculty and staff, whether the applicant appears on the Dean’s or Director’s interest list, standardized test scores, and the
proportion of the applicant’s high school and neighborhood that is African-American, Hispanic, and White. In addition, recruited athletes
are reassigned to rating combinations in the regression sample that contain the next highest athletic rating. Applicants with certain
socioeconomic characteristics are given a low-SES boost by adding a value to their admission index. The value is equal to 0.5 multiplied by
a given integer multiplier, multiplied by the number of characteristics an applicant displays out of the following: disadvantaged, requested
a fee waiver, first generation college student, neighborhood median income less than or equal to $65,000. Further, the number of
disadvantaged applicants is doubled.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 127

7.3.6. Place-based policies like “percent plans”
7.3.6.1. Place based policies would be difficult to implement
264. Another race-neutral tool for increasing racial diversity identified in the literature is the
use of “place-based” admission policies. SFFA and Mr. Kahlenberg both advocate the use of placebased admissions. SFFA’s Complaint asserts that Harvard could use place-based preferences to take
advantage of community-based homogeneity in race—that is, residential segregation—fostering a
diverse class without using race as a factor in admissions. SFFA suggests Harvard could use a
“percent plan” like the “top ten percent rule” employed by the University of Texas, under which
admission at a University of Texas school is available to the top ten percent of graduating seniors at
each public high school in the state. SFFA and Mr. Kahlenberg also suggest that Harvard could admit
the top students from each ZIP code.200 For example, Mr. Kahlenberg simulates a model of
admissions based on admitting an equal number of applicants from each of 33 College Board
neighborhood “clusters.”201
265. For at least two reasons, the use of place-based practices like these would likely be
impracticable for Harvard. First, place-based practices generally rely on quantitative formulas to
determine which student is the “best” in a given high school, neighborhood, or other geographic unit.
As discussed earlier, however, I understand that Harvard believes strongly in the importance of a
whole-person review that examines the many and varied types of excellence that applicants can bring
to campus. Students admitted to Harvard are not all the “best” in the same way; Harvard considers
them to be the “best” in many different ways, which an algorithm could not capture. To determine
which student in each high school or ZIP code was the “best” on the broader array of dimensions
considered in its whole-person process, Harvard would have to apply its whole-person process at the
outset, simply to identify which “top” student to admit. That would be extraordinarily labor-intensive,
particularly given that any plan like this would likely draw a sharply increased number of applicants.
And it would also ignore the fact that Harvard pursues excellences of many kinds, not excellence on a
single dimension. Even within the whole-person process, that is, there is no such thing as a “best”
student; there are, rather, students who excel in many different ways.

200

Note that these types of place-based polices rely on allocating slots to students from different geographies (or types of
geographies); these policies differ from simply considering an applicant’s high school and neighborhood as one of many
pieces of information in a whole-person admissions process.
201
There are 33 College Board neighborhood clusters, but Mr. Kahlenberg creates an extra cluster for applicants missing
College Board data, and combines a small cluster with the extra “cluster” of applicants missing College Board data.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 128

266. Second, there are many times more high schools, cities, and ZIP codes represented in the
applicant pool than there are slots in the incoming class. Harvard does not serve a limited geography;
its reach is national and global. Even if Harvard admitted only the “top” student from each U.S. high
school represented in its applicant pool—and even if it were simple to identify the “top” student,
which it is not, for the reason discussed above202—the result would be a class four to five times larger
than Harvard’s actual class. The class of 2019, for example, had 1,719 admitted students from
domestic high schools—but over 7,500 U.S. high schools were represented in the applicant pool,
spanning over 4,000 cities and towns (see Exhibit 48).203 Reserving even one slot per high school is
not feasible for Harvard, even if one limits the exercise to the subset of U.S. high schools currently
represented in Harvard’s applicant pool. As noted in Exhibit 48, there are over 41,000 high schools
across the United States, and more than 33,000 ZIP codes. Moreover, if Harvard were to publicize a
policy to offer admission to the top student from each high school or ZIP code, it would likely see a
massive increase in the number of applications, generating substantial costs to review them.
267. In light of the sheer number of schools and geographic locations represented in its
applicant pool, Harvard would need to either draw randomly from among top students across places,
or combine a place-based percent plan with additional algorithmic screens for desirable
characteristics (which would deprive it of the chance to identify dimensions of achievement that are
not easily quantified) or a whole-person review applied to the entire pool of “top students.”
268. Crucially, any such approach would preclude Harvard from admitting multiple
candidates from the same high school or neighborhood, even though, say, the tenth student Harvard
admits from a given high school or neighborhood might well bring more to campus than the top
student in some other high school or neighborhood.

202

Mr. Kahlenberg ranks applicants within a cluster based on their predicted probability of admission, computed using his
full admissions model. This is hardly practical: his proposal would effectively entail conducting the entire Harvard
admissions process, then ranking the entire applicant pool within geographies. Currently, officers do not rank all
applicants in the pool, nor is there a workable method for selecting the “best” student from a high school or ZIP code
when Harvard seeks excellence of so many kinds.
203
There are 4,006 unique geographic locations represented among applicants to the class of 2019, as determined by
applicants’ high school location. These geographic locations vary in size and include cities, towns, neighborhoods, and
unincorporated communities. Because many cities and towns contain more than one ZIP code, there are likely far more
than 4,006 ZIP codes represented in the Harvard applicant pool. See workpaper.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 129

There are far more high schools and ZIP codes than slots in Harvard’s admitted class

Source: Augmented Arcidiacono Data; National Center for Education Statistics; U.S. Census Data
Note: Sample consists of all applicants to the class of 2019 on domestic dockets in Prof. Arcidiacono’s corrected expanded sample. Total
number of public and private high schools in 2013 – 2014 (i.e., classes of 2017 – 2018) includes schools with both elementary and
secondary grades. ZIP Code Tabulation Areas are representations of ZIP code service areas created by the U.S. Census Bureau to represent
statistical data from censuses and surveys. Total number of ZIP Code Tabulation Areas in 2016 is shown

269. Notwithstanding the infeasibility of the practices discussed above, I examined how the
racial diversity and other characteristics of Harvard’s admitted class would be expected to change if
Harvard implemented a place-based admission policy that focused on “top students” from each high
school.204 As I show below, such a practice would not be effective in both fostering racial diversity
and preserving class quality.
270. A primary aim of place-based admissions practices is to use the reality of residential
segregation to increase representation from underrepresented groups. To that end, in addition to
studying the characteristics of all top students, I also examine the characteristics of all top students
coming from public high schools (the traditional target for percent plans), low-income high schools

204

For the purpose of this exercise, I rank all students within a high school by the sum of their profile ratings, and refer to
the pool of top-ranked students as “top students.” This is a parsimonious way to rank students while still paying some
attention to Harvard’s desire for whole-person evaluation, but it is obviously a crude way of identifying the “top” student.
The exercise of ranking “top” students in this way does not suggest that doing so would actually fulfill Harvard’s
educational objective of admitting students who embody many and varied forms of excellence.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 130

(defined as high schools with a median household income less than $65,000), and rural high schools.
271. As shown in Exhibit 49, the pool of top students at all high schools contains roughly the
same fraction of AHO applicants as the actual pool of admitted students, but somewhat fewer AsianAmerican applicants. Top students from public schools are similarly racially diverse. Top students
from low-income schools are heavily AHO. In contrast, the pool of top students from rural schools is
predominantly White, with few Asian-American students. These figures suggest that filling some (or
all) of Harvard’s class with top students from United States high schools, public high schools, or lowincome high schools could in principle help produce a racially diverse class. Tapping top rural talent
is unlikely to foster racial diversity.

Racial composition of top students, applicants to the class of 2019

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Sample consists of all applicants to the class of 2019 on domestic dockets in Prof. Arcidiacono’s corrected expanded sample.
Students are ranked based on a sum of the four profile ratings: academic, athletic, extracurricular, and personal. The top students are
selected based on rank within each school, allowing for ties. Low-income schools are defined as schools with average median income less
than or equal to $65,000.

272. However, admitting students in this manner would likely cause a sharp decline in the
quality of the admitted class. As evidenced by Exhibit 50, the pools of top students in Harvard’s
data—whether from all high schools, public schools, low-income schools, or rural schools—have
markedly lower profile ratings (on all four dimensions), lower SAT and ACT scores, and lower
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 131

Academic Index values than the current pool of admitted students. The main reason for this decline in
quality is that, by admitting only the top student from each school, Harvard is forced to replace a
large number of excellent applicants from the most competitive high schools in the country with top
students from high schools that are substantially weaker.
273. Taken together, this evidence indicates that place-based admissions practices that
reserve slots for top performers at each high school could generate racial diversity, but at the expense
of the quality of the admitted class.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 132

Percent plans would likely decrease the quality of the admitted class

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Sample consists of all applicants to the class of 2019 on domestic dockets in Prof. Arcidiacono’s corrected expanded sample.
Students are ranked based on a sum of the four profile ratings: academic, athletic, extracurricular, and personal. The top students are
selected based on rank within each school, allowing for ties. Low-income schools are defined as schools with average median income less
than or equal to $65,000..

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 133

7.3.6.2. Implementing neighborhood cluster-based admissions in conjunction with other race-neutral
policies
274. In his report, Mr. Kahlenberg includes simulations in which Harvard abandons its wholeperson admissions process and instead admits an equal number of students from each of the College
Board’s neighborhood clusters. In the same simulations, Mr. Kahlenberg also gives disadvantaged
students an admissions preference, and assumes that Harvard could double its pool of disadvantaged
applicants through outreach and recruiting.
275. Building on the simulations above, I, too, consider how the admitted class would change
under such a regime. As in Mr. Kahlenberg’s report, I rank students within a cluster based on their
estimated probability of admission. I generate the ranking using a model of admissions in which
Harvard admissions officers (1) do not consider race, lineage status, whether an applicant is an
athletic recruit, whether an applicant is a child of Harvard faculty or staff, or whether an applicant is
on the Dean’s or Director’s interest list, (2) afford a preference to low-SES applicants as discussed in
Section 7.3.3 above, and (3) eliminate consideration of standardized test scores. Furthermore, I
assume that through increased recruiting and outreach Harvard could double the number of
disadvantaged applicants in its pool, although that assumption is unrealistic for the reasons discussed
above.
276. As suggested by my findings in Section 7.3.6.1, place-based admissions could help
foster racial diversity, but only at a steep cost to the quality of the admitted class. Each bar in Exhibit
51 depicts the racial composition of the expected class under a neighborhood-based admissions
policy for a given boost for low-SES applicants. Recall that in addition to using cluster-based
admissions, I have already eliminated consideration of test scores, and I am also assuming Harvard
could double its pool of disadvantaged applicants through outreach; as a result, a lesser low-SES
boost is required to attain a given level of AHO representation. Even in this conservative scenario,
however, generating a proportion of African-American students comparable to that of the current
class requires a significant low-SES boost. This combination of practices would be expected to
increase the proportion of Hispanic students relative to the current class. The proportion of AsianAmerican students would stay relatively constant. The fraction of White students also falls in this
simulation.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 134

Admitting an equal number of students across neighborhood clusters, in conjunction with other
race-neutral policies: Racial composition

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Sample consists of applicants to the class of 2019 in Prof. Arcidiacono’s corrected expanded sample who are in my preferred year-byyear regression model. Simulation eliminates consideration of race, lineage status, recruited-athlete status, whether an applicant’s parents
are Harvard faculty and staff, whether the applicant appears on the Dean’s or Director’s interest list, standardized test scores, and the
proportion of the applicant’s high school and neighborhood that is African-American, Hispanic, and White. In addition, recruited athletes
are reassigned to rating combinations in the regression sample that contain the next highest athletic rating. Applicants with certain
socioeconomic characteristics are given a low-SES boost by adding a value to their admission index. The value is equal to 0.5 multiplied by
a given integer multiplier, multiplied by the number of characteristics an applicant displays out of the following: disadvantaged, requested
a fee waiver, first generation college student, neighborhood median income less than or equal to $65,000. Further, the number of
disadvantaged applicants is doubled. Applicants are ranked in descending order of admission index within each neighborhood cluster and
an approximately equal number of applicants are admitted from each cluster to fill the class.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 135

Admitting an equal number of students across neighborhood clusters, in conjunction with other
race-neutral policies: Changes in racial composition
Predicted Class Without Consideration of Race and Factors that Allegedly Advantage White
Applicants, Change from Actual Class

Race
1. White

Actual
Admitted
Class

1x
Low-SES
Boost

2x
Low-SES
Boost

3x
Low-SES
Boost

4x
Low-SES
Boost

5x
Low-SES
Boost

6x
Low-SES
Boost

10x
Low-SES
Boost
-159

676

-62

-72

-96

-107

-117

-122

2. Asian-American

402

+12

-4

+11

+16

+27

+16

+21

3. Hispanic or Other

233

+77

+101

+110

+117

+113

+124

+150

4. African-American

234

-14

-5

+4

+10

+13

+17

+24

5. Race Missing

134

-13

-20

-29

-36

-36

-35

-36

Predicted Class Without Consideration of Race and Factors that Allegedly Advantage White
Applicants, % Change from Actual Class

Race
1. White

Actual
Admitted
Class

1x
Low-SES
Boost

2x
Low-SES
Boost

3x
Low-SES
Boost

4x
Low-SES
Boost

5x
Low-SES
Boost

6x
Low-SES
Boost

10x
Low-SES
Boost
-24%

40%

-9%

-11%

-14%

-16%

-17%

-18%

2. Asian-American

24%

+3%

-1%

+3%

+4%

+7%

+4%

+5%

3. Hispanic or Other

14%

+33%

+43%

+47%

+50%

+48%

+53%

+64%

4. African-American

14%

-6%

-2%

+2%

+4%

+6%

+7%

+10%

8%

-10%

-15%

-22%

-27%

-27%

-26%

-27%

5. Race Missing

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Sample consists of applicants to the class of 2019 in Prof. Arcidiacono’s corrected expanded sample who are in my preferred year-byyear regression model. Simulation eliminates consideration of race, lineage status, recruited-athlete status, whether an applicant’s parents
are Harvard faculty and staff, whether the applicant appears on the Dean’s or Director’s interest list, standardized test scores, and the
proportion of the applicant’s high school and neighborhood that is African-American, Hispanic, and White. In addition, recruited athletes
are reassigned to rating combinations in the regression sample that contain the next highest athletic rating. Applicants with certain
socioeconomic characteristics are given a low-SES boost by adding a value to their admission index. The value is equal to 0.5 multiplied by
a given integer multiplier, multiplied by the number of characteristics an applicant displays out of the following: disadvantaged, requested
a fee waiver, first generation college student, neighborhood median income less than or equal to $65,000. Further, the number of
disadvantaged applicants is doubled. Applicants are ranked in descending order of admission index within each neighborhood cluster and
an approximately equal number of applicants are admitted from each cluster to fill the class.

277. The racial diversity attained through the set of practices simulated in Exhibit 51 and
Exhibit 52 comes at a high cost. Exhibit 53 depicts the simulated change in class composition
associated with using a cluster-based admissions process in conjunction with other race-neutral
alternatives. Attaining a proportion of AHO students comparable to that of the current class (the 1x
results) is associated with a 17% decline in the fraction of applicants with top academic ratings.
Generating a proportion of African-American students comparable to that of the current class is
associated with more than a 23% decline in the fraction of admitted students with top academic
ratings. In either scenario, the number of recruited athletes and lineage students declines sharply
(Exhibit 53), and the financial need of admitted students increases (Exhibit 54).

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 136

Admitting an equal number of students across neighborhood clusters, in conjunction with other
race-neutral policies: Changes in class quality
Predicted Class Without Consideration of Race and Factors that
Allegedly Advantage White Applicants
1x Low-SES Boost

2x Low-SES Boost

3x Low-SES Boost

Actual
Admitted
Class
Outcome Measures
1.
2.
3.
4.
5.

Race
White
Asian-American
Hispanic or Other
African-American
Race Missing

6.
7.
8.
9.

Academic
Average Composite SAT Score
Average Composite ACT Score
Average Converted GPA
Average Academic Index

10.
11.
12.
13.

Predicted
Value

% Change

Predicted
Value

% Change

Predicted
Value

% Change

[A]

[B]

([B]-[A])/[A]

[C]

([C]-[A])/[A]

[D]

([D]-[A])/[A]

676
402
233
234
134

614
414
310
220
121

-9%
+3%
+33%
-6%
-10%

604
398
334
229
114

-11%
-1%
+43%
-2%
-15%

580
413
343
238
105

-14%
+3%
+47%
+2%
-22%

2244
33.1
77.0
228

2163
32.5
76.5
223

-4%
-2%
-1%
-2%

2149
32.2
76.5
222

-4%
-3%
-1%
-2%

2136
32.0
76.3
221

-5%
-3%
-1%
-3%

Fraction with Profile Rating of 1 or 2
Academic
76%
Extracurricular
62%
Personal
71%
Athletic
27%

63%
60%
73%
20%

-17%
-3%
+3%
-25%

59%
56%
70%
20%

-23%
-10%
-1%
-24%

54%
53%
67%
19%

-29%
-15%
-6%
-30%

259

67

-74%

61

-76%

47

-82%

72

13

-82%

13

-82%

11

-85%

180

94

-48%

95

-47%

94

-48%

44

12

-73%

12

-73%

10

-77%

839

846

+1%

843

+0.5%

843

+0.5%

Applicant Characteristics
14. Number of Lineage Students
Number of Double Lineage
15.
Students
16. Number of Recruited Athletes
17.

Number of Children of Harvard
Faculty and Staff

18.

Number of Students on Dean’s
and Director’s Interest Lists

19. Number of Female Students

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 137

20.
21.
22.
23.
24.
25.
26.
27.

Concentration
Social Sciences
Humanities
Biological Sciences
Physical Science
Engineering
Computer Science
Mathematics
Unspecified

25%
15%
21%
7%
13%
6%
6%
7%

24%
14%
22%
8%
13%
5%
7%
7%

-3%
-7%
+4%
+11%
+3%
-20%
+12%
+1%

24%
13%
23%
9%
13%
5%
7%
7%

-3%
-10%
+9%
+16%
0.00%
-21%
+4%
+3%

24%
13%
23%
8%
13%
5%
6%
7%

-4%
-9%
+10%
+12%
+3%
-19%
-3%
+4%

28.
29.
30.
31.
32.

Geography
Number Rural
Number in Northeast
Number in Midwest
Number in South
Number in West

59
694
207
379
399

130
590
279
435
375

+120%
-15%
+35%
+15%
-6%

133
592
274
431
382

+125%
-15%
+32%
+14%
-4%

137
593
279
438
369

+132%
-15%
+35%
+16%
-8%

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Sample consists of applicants to the class of 2019 in Prof. Arcidiacono’s corrected expanded sample who are in my preferred year-byyear regression model. Simulation eliminates consideration of race, lineage status, recruited-athlete status, whether an applicant’s parents
are Harvard faculty and staff, whether the applicant appears on the Dean’s or Director’s interest list, standardized test scores, and the
proportion of the applicant’s high school and neighborhood that is African-American, Hispanic, and White. In addition, recruited athletes
are reassigned to rating combinations in the regression sample that contain the next highest athletic rating. Applicants with certain
socioeconomic characteristics are given a low-SES boost by adding a value to their admission index. The value is equal to 0.5 multiplied by
a given integer multiplier, multiplied by the number of characteristics an applicant displays out of the following: disadvantaged, requested
a fee waiver, first generation college student, neighborhood median income less than or equal to $65,000. Further, the number of
disadvantaged applicants is doubled. Applicants are ranked in descending order of admission index within each neighborhood cluster and
an approximately equal number of applicants are admitted from each cluster to fill the class.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 138

Admitting an equal number of students across neighborhood clusters, in conjunction with other
race-neutral policies: Changes in financial need
Predicted Class Without Consideration of Race and Factors that Allegedly
Advantage White Applicants
1x Low-SES Boost

Socioeconomic Status
1. Number First Generation College

Actual
Admitted
Class
[A]

Predicted
Value
[B]

% Change
([B]-[A])/[A]
+187%

2x Low-SES Boost
Predicted
Value
[C]

Predicted
Value
[D]
530

% Change
([D]-[A])/[A]
+342%

120

344

2. Number Disadvantaged

297

906

+205%

1047

+253%

1176

+296%

3. Number with Fee Waiver

309

853

+176%

1000

+224%

1127

+265%

1102

1431

+30%

1471

+33%

1507

+37%

4. Number with Financial Aid

427

% Change
([C]-[A])/[A]
+256%

3x Low-SES Boost

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Sample consists of applicants to the class of 2019 in Prof. Arcidiacono’s corrected expanded sample who are in my preferred year-byyear regression model. Simulation eliminates consideration of race, lineage status, recruited-athlete status, whether an applicant’s parents
are Harvard faculty and staff, whether the applicant appears on the Dean’s or Director’s interest list, standardized test scores, and the
proportion of the applicant’s high school and neighborhood that is African-American, Hispanic, and White. In addition, recruited athletes
are reassigned to rating combinations in the regression sample that contain the next highest athletic rating. Applicants with certain
socioeconomic characteristics are given a low-SES boost by adding a value to their admission index. The value is equal to 0.5 multiplied by
a given integer multiplier, multiplied by the number of characteristics an applicant displays out of the following: disadvantaged, requested
a fee waiver, first generation college student, neighborhood median income less than or equal to $65,000. Further, the number of
disadvantaged applicants is doubled. Applicants are ranked in descending order of admission index within each neighborhood cluster and
an approximately equal number of applicants are admitted from each cluster to fill the class.

7.3.7. Increasing financial aid
278. Mr. Kahlenberg also identifies generous financial aid as a way to expand racial diversity,
on the theory that some talented low-SES applicants, many of whom are AHO, do not apply to or
matriculate at Harvard because of expected costs but would apply or matriculate if Harvard were
more affordable.205 But available evidence suggests that increasing aid beyond Harvard’s current
generous levels would not produce additional racial diversity among applicants or matriculants.
279. Harvard already offers exceptionally generous financial aid. Its financial aid program is
designed to “ensure that students admitted to Harvard are not prevented from matriculating due to
their financial circumstances,” and “to ensure that Harvard will be affordable to every student.”206
“Currently, Harvard expects no parental contribution for students from families with typical assets
and annual incomes below $65,000. Families with typical assets and incomes between $65,000 and
205
206

Kahlenberg Report, pp. 29–31.
Second Interrogatory Response, p. 16.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 139

$150,000 are generally expected to contribute between 0–10% of their income, and Harvard does not
consider the family’s home equity in calculating family resources.”207
280. These policies are generous enough to make Harvard more affordable to low-income
applicants than many public institutions. Harvard estimates that “[n]inety percent of American
families would pay the same or less to send their children to Harvard as they would a state school.”208
When the New York Times ranked 171 top public and private colleges based on the number of
middle- and low-income students served and the net amount charged to those students, Harvard came
in tenth, ahead of all of its Ivy League peers. Based on net-price calculations for middle-income
students only, Harvard came in second, behind only Stanford.209 Harvard’s current threshold for zero
parental contribution is as generous as or more generous than the limits at Ivy League peers and
Stanford.210 Approximately 70% of African-American households and more than 60% of all Hispanic
households are already eligible for zero parental contribution.211
281. Harvard’s current financial aid program is the culmination of a decade of financial aid
initiatives, summarized in Exhibit 55. These staggered changes in the aid thresholds provide a natural
experiment: they allow me to assess whether historical increases in financial aid drew in AHO
applicants, contributed to an increase in the share of AHO admitted students, or helped boost
matriculation of AHO admitted students. These historical patterns can also shed light on what might
happen to the pools of applicants and admitted students if Harvard were to expand its financial aid
207

Second Interrogatory Response, p. 16.
Harvard College, Financial Aid Fact Sheets, available at https://college.harvard.edu/financial-aid/how-aid-works/factsheet, accessed December 12, 2017.
209
New York Times, “Top Colleges Doing the Most for the American Dream,” available at
https://www.nytimes.com/interactive/2017/05/25/sunday-review/opinion-pell-table.html, accessed November 19, 2017
(“The ranking is based on a combination of the number of lower-and middle-income students that a college enrolls and
the price it charges these students. The top of the ranking is dominated by campuses in the University of California
system, while the most diverse private colleges include Amherst, Pomona, Harvard and Vassar.”).
210
While Dartmouth offers free tuition for families whose total income is $100,000 or less, the other Ivy League
institutions and Stanford cover all educational costs (including tuition, room, board, books, etc.) in their aid for families
falling at or below their zero parental contribution thresholds. Penn Student Registration & Financial Services, “A Look
at the Facts,” available at http://www.sfs.upenn.edu/paying/paying-pro-look-at-the-facts.htm, accessed December 5, 2017;
Brown University, “General Questions,” available at https://www.brown.edu/about/administration/financial-aid/generalquestions, accessed December 5, 2017; Columbia University, “How Aid Works,” available at https://ccseas.financialaid.columbia.edu/how/aid/works, accessed December 5, 2017; Cornell University, “Financial Aid, Financial
Aid Initiative,” available at https://finaid.cornell.edu/cost-attend/financial-aid-initiatives, accessed December 5, 2017;
Yale University, “Financial Aid In-Depth,” available at https://admissions.yale.edu/financial-aid-prospective-students,
accessed December 5, 2017; Princeton University, “How Princeton’s Aid Program Works,” available at
https://admission.princeton.edu/cost-aid/how-princetons-aid-program-works, accessed December 5, 2017; Stanford
University, “How Aid Works,” available at https://financialaid.stanford.edu/undergrad/how/parent.html, accessed
December 5, 2017; Dartmouth College, “How Aid Works,” available at http://admissions.dartmouth.edu/financialaid/how-aid-works/how-much-help-will-i-get, accessed December 5, 2017.
211
See workpaper based on data from the U.S. Census Bureau, Current Population Survey 2017 Annual Social and
Economic Supplement.
208

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 140

offerings further. Surprisingly, Mr. Kahlenberg failed to consider this historical evidence of how his
proposed race-neutral alternatives have fared in practice.

Timeline of Harvard’s changes to financial aid policies, by class

Source: HARV00031667; HARV00016122; HARV00010744; HARV00067792

282. In the following set of exhibits, I examine how three key outcomes have changed as
Harvard expanded its financial aid: number of applicants of each race, number of admitted students
of each race, and matriculation rates by race. I find that the fraction of AHO applicants (among all
applicants) rose and then plateaued as Harvard expanded financial aid (Exhibit 56). Importantly, the
most recent expansion of financial aid did not result in an increase in the share of AHO applicants.
The fraction of Asian-American applicants rose slightly, while the fraction of applicants who are
White fell.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 141

Share of African-American, Hispanic, or Other applicants rose, then plateaued, as Harvard
expanded financial aid

Source: HARV00032509 – HARV00032524; HARV00031667; HARV00016122; HARV00010744; HARV00067792; Augmented
Arcidiacono Data
Note: Applicants classified as “Native American / Hawaiian” are grouped with applicants classified as “Other.” Data for classes 2018 –
2019 are reconstructed using a methodology that replicates the produced aggregate data for classes 2000 – 2017.

283. The fraction of AHO applicants who applied for financial aid also did not increase after
the most recent expansion (Exhibit 57). This suggests that financial aid is not a limiting factor for
AHO applicants. This pattern holds for applicants of other races as well.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 142

Most recent increase in the threshold for zero parental contribution did not increase fraction of
African-American, Hispanic, or Other applicants applying for financial aid

Source: Augmented Arcidiacono Data
Note: Applicants classified as “Native American / Hawaiian” are grouped with applicants classified as “Other.” Racial categories are
constructed using a methodology that replicates the racial categories in the produced aggregate data.

284. Increases in financial aid were also not closely linked to changes in the fraction of AHO
or Asian-American applicants among admitted students (Exhibit 58). The most recent expansion of
financial aid for the class of 2016 did not increase the share of admitted students who are AHO.
Expansions in financial aid were also not consistently associated with increases in matriculation of
AHO admitted students relative to other races (Exhibit 59).

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 143

Share of African-American, Hispanic, or Other admitted students has risen over time, but not in
close step with expansions in financial aid

Source: HARV00032509 – HARV00032524; HARV00031667; HARV00016122; HARV00010744; HARV00067792; Augmented
Arcidiacono Data
Note: Applicants classified as “Native American / Hawaiian” are grouped with applicants classified as “Other.” Data for classes 2018 –
2019 are reconstructed using a methodology that replicates the produced aggregate data for classes 2000 – 2017.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 144

Expansions in financial aid did not consistently affect matriculation of African-American,
Hispanic, or Other admitted students

Source: HARV00032509 – HARV00032524; HARV00031667; HARV00016122; HARV00010744; HARV00067792; Augmented
Arcidiacono Data
Note: Applicants classified as “Native American / Hawaiian” are grouped with applicants classified as “Other.” Data for classes 2018 –
2019 are reconstructed using a methodology that replicates the produced aggregate data for classes 2000 – 2017.

285. These results indicate it is unlikely that Harvard would be able to increase its proportion
of AHO students (relative to a regime of not considering race) by offering more generous financial
aid. Even with Harvard’s continuous expansion of its financial aid program, the share of applicants
who are AHO has not risen markedly over the past eight years. That is, perhaps, not surprising when
one considers the current income distribution in the United States. The vast majority of households in
the $60,000–65,000, $65,000–70,000, and $70,000–75,000 income brackets are not AHO, and there
are fewer and fewer AHO households as one moves up those brackets. Given the distribution of
income by race, incremental expansions in the $65,000 threshold for zero parental contribution are
likely to disproportionately attract White applicants, not AHO applicants.212

212

See workpaper based on data from the U.S. Census Bureau, Current Population Survey 2017 Annual Social and
Economic Supplement.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 145

7.3.8. Eliminating Early Action
286. The academic literature and Mr. Kahlenberg suggest that Early Action admissions could
potentially privilege applicants with more resources and better college guidance counseling. Because
these students are more likely to be White, Mr. Kahlenberg argues that eliminating Early Action
could enhance racial diversity at Harvard.213 This claim is directly testable, because Harvard both
eliminated and reinstated Early Action in recent years, allowing researchers to observe the effect of
such changes on the pool of applicants and admitted students. Had Mr. Kahlenberg examined this
historical evidence, he would have found that Harvard’s experience indicates that abolishing Early
Action once again is unlikely to increase racial diversity.
287. To assess the effect of abolishing Early Action on racial diversity, I look at the racial
composition of Harvard’s applicants, admitted students, and matriculants before and after Harvard
implemented key changes in its Early Action policy. Exhibit 60 summarizes those changes. Harvard
offered some form of Early Action through the class of 2011.214 Harvard eliminated Early Action for
the class of 2012, and then reinstated it for applicants to the class of 2016. Those changes in policy
provide a natural experiment to test Mr. Kahlenberg’s claim that eliminating Early Action could
increase racial diversity at Harvard. I focus on comparing the period during which Harvard abolished
Early Action to the more recent period after Harvard reinstated Early Action, as financial aid policies
were more similar during these periods than in earlier years.

Timeline of Harvard’s changes to Early Action policies, by class

Source: HARV00031695

213

Kahlenberg Report, pp. 42–44; Complaint, pp. 85–86; Julie J. Park and M. Kevin Eagan, “Who Goes Early? A MultiLevel Analysis of Enrolling via Early Action and Early Decision Admissions,” Teachers College Record, 113(11), 2011;
pp. 2345–2373 at pp. 2358, 2365, and 2368; Christopher Avery and Jonathan Levin, “Early Admission at Selective
Colleges,” Stanford Institute for Economic Policy Research No. 08–31 March 2009, pp. 1–36 at p. 4.
214
In the class of 2007 admissions cycle, Harvard permitted applicants to apply early to multiple institutions, but reverted
to single-choice early action for the classes of 2008 to 2011.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 146

288. I start my analysis by looking at whether reinstating Early Action had an effect on the
racial composition of applicants (Exhibit 61). As Exhibit 61 shows, the fraction of AHO applicants in
the applicant pool rose steadily from the class of 2000 through roughly the class of 2012, when Early
Action was abolished. The fraction of AHO applicants then plateaued in more recent years, averaging
26% during the period Early Action was abolished (classes of 2012 – 2015) and 26% after Early
Action was reinstated.215 The fact that reinstating Early Action has not led to a meaningful decrease
in the fraction of applicants who are AHO suggests that abolishing Early Action again would not be
likely to increase the fraction of AHO students in the applicant pool.
289. The fraction of Asian-American applicants increased a small amount between the classes
of 2000 – 2011, dipped slightly during the first two years in which Early Action was abolished (2012
– 2013), then rose again. The share of applicants who are White generally fell throughout the 2000 –
2019 period, jumping only briefly in the class of 2014.

215

See workpaper.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 147

Reinstating Early Action did not have an effect on the share of applicants who are AfricanAmerican, Hispanic, or Other

Source: HARV00032509 – 24; HARV00031695; Augmented Arcidiacono Data
Note: Applicants classified as “Native American / Hawaiian” are grouped with applicants classified as “Other.” Data for classes 2018 –
2019 are reconstructed using a methodology that replicates the produced aggregate data for classes 2000 – 2017.

290. I also look at how reinstating Early Action affected the racial composition of admitted
students. Exhibit 62 shows how the racial composition of admitted students has evolved over the past
20 years. Focusing on the recent period with and without Early Action, the exhibit shows that, on
average, AHO admitted students comprised 25% of the entering classes without Early Action (2012 –
2015); and after Early Action was reinstated, AHO admitted students comprised 26% of the entering
classes.216 Similarly, the fraction of admitted students who are Asian-American did not fall when
Early Action was reinstated.

216

See workpaper.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 148

Reinstating Early Action did not decrease the share of admitted students who are Hispanic,
African-American, or Other

Source: HARV00032509 – 24; HARV00031695; Augmented Arcidiacono Data
Note: Applicants classified as “Native American / Hawaiian” are grouped with applicants classified as “Other.” Data for classes 2018 –
2019 are reconstructed using a methodology that replicates the produced aggregate data for classes 2000 – 2017.

291. Finally, I also examine how reinstating Early Action affected matriculation rates for
admitted students of different races. The patterns in Exhibit 63 undermine SFFA’s suggestion that
eliminating Early Action would increase racial diversity on campus. Matriculation rates rose for
admitted students of all races after Early Action was reinstated for the class of 2016. Asian-American
matriculation rates rose from 83% in the period with no Early Action to 85% after Early Action was
reinstated. Matriculation rates also rose for White admitted students and those whose race is
unknown.
292. Matriculation rates for AHO admitted students averaged about 70% for classes before
Early Action was abolished (2000 – 2011), but dropped to an average of 65% during the years Early
Action was eliminated (2012 – 2015). Matriculation rates returned to an average of about 71% for the
classes after Early Action was reinstated (2016 – 2019).
293. That trend as to the matriculation rates of AHO admitted students was, in fact, a key
reason Harvard chose to reinstitute Early Action, according to documents produced in this litigation.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 149

Based on its own statistical analysis, Harvard concluded that top AHO applicants were more likely to
apply to and matriculate at universities that offered them the option to apply early, and that Harvard
was losing such applicants (both in their choice of whether to apply and in their choice of whether to
matriculate) when it did not offer the Early Action option.217 Harvard thus viewed restoring Early
Action as a race-neutral way to better capture top AHO talent.218 In sum, Harvard’s historical
experience suggests that abolishing Early Action would not help foster a racially diverse student
body.219

Admitted students are more likely to matriculate under Early Action

Source: HARV00032509 – 24; HARV00031695; Augmented Arcidiacono Data
Note: Applicants classified as “Native American / Hawaiian” are grouped with applicants classified as “Other.” Data for classes 2018 –
2019 are reconstructed using a methodology that replicates the produced aggregate data for classes 2000 – 2017.

217

OIR Presentation at HARV00031694, HARV00031701.
Memo from President Faust and Dean Smith to Members of the Corporation, “Proposed Changes in Admissions
Policy,” February 2, 2011, HARV00030303 – 32 at HARV00030305 – 06, HARV00030325 – 28; OIR Presentation at
HARV00031694, HARV00031701; Second Interrogatory Response, pp. 17–18.
219
In his report, Mr. Kahlenberg uses a simulation approach in which he turns off the coefficient on the Early Action
indicator, which is large and positive. The logic of Mr. Kahlenberg’s approach is that if the apparent positive effect on
likelihood of admission associated with applying early simply reflects unobserved privilege and resources, then removing
that positive effect could help foster diversity. The problem with this approach is that the estimated positive effect may in
fact reflect valuable, unobserved differences in the characteristics and quality of Early Action versus Regular Decision
applicants. For example, applicants who apply early to Harvard cannot apply early elsewhere. As a result, it could be that
they do more research on the institution, and therefore make a more compelling case for Harvard’s being a good match
for their particular academic and extracurricular interests. Applying Early Action could also be a valuable signal of
commitment to attending Harvard. If that is true, then turning off the Early Action preference would discard the value
Harvard places on these important differentiators. For these reasons, I do not use a simulation approach to evaluating the
impact of eliminating Early Action. I examine the historical record instead.
218

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 150

7.4. Mr. Kahlenberg’s simulated race-neutral practices, like others considered above, could achieve a
comparably diverse class only by changing the class in significant ways and compromising its quality
294. In this section, I address the five combinations of race-neutral admissions practices that
Mr. Kahlenberg simulates. The five simulations all focus on four types of race-neutral alternatives:
(1) eliminating consideration of factors that allegedly favor White applicants (consideration of
whether an applicant is a lineage applicant, recruited athlete, child of Harvard faculty or staff, Early
Action applicant, or on the Dean’s or Director’s interest lists), (2) affording a preference to low-SES
candidates, (3) admitting candidates based on location, and (4) increasing the pool of disadvantaged
applicants through increases in recruiting or financial aid.
295. As I discuss below, the combinations of race-neutral practices that Mr. Kahlenberg
simulates—like all those discussed above—either would not enable Harvard to achieve a comparably
diverse class or would enable Harvard to achieve a comparably diverse class only at a significant cost
to the quality of the class.
7.4.1. An overview of Mr. Kahlenberg’s simulations
296. Mr. Kahlenberg presents the results of five simulations in his report. The simulations
were actually conducted by Prof. Arcidiacono, using a version of Prof. Arcidiacono’s logit model of
admissions that includes the personal rating. All five simulations begin in the same way. Mr.
Kahlenberg first turns off the coefficients associated with race, as well as the coefficients for being
disadvantaged, having applied for financial aid, being a first-generation applicant, receiving a fee
waiver, being an athletic recruit, being a lineage applicant, being a child of Harvard faculty or staff,
applying Early Action, and appearing on the Dean’s or Director’s interest lists. He then simulates
other changes to the admissions process as follows:
• Simulation 1: He gives each “disadvantaged” applicant (i.e., each
applicant identified as disadvantaged by Harvard’s admissions officers)
a preference equivalent to half the preference that recruited athletes are
estimated to receive.
• Simulation 2: In addition to Simulation 1, he simulates the effect of
increased recruiting and financial aid by artificially doubling the number
of disadvantaged applicants in the pool.
• Simulation 3: In addition to Simulation 1, he simulates a place-based
admissions practice by using his adjusted model to rank applicants

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 151

within each College Board neighborhood cluster, then admitting an
equal number of the highest-ranked students from each cluster.
• Simulation 4: He repeats Simulation 3, but allows recruited athletes to
retain the admissions advantage they are estimated to receive. This is the
simulation on which Mr. Kahlenberg focuses in his report.
• Simulation 5: He repeats Simulation 3, but simulates the effect of
increased recruiting and financial aid by artificially doubling the number
of disadvantaged applicants in the pool.
297. Mr. Kahlenberg’s simulations are effectively variations on the theme of those I
conducted. He and I use a similar methodology to simulate the effects of race-neutral alternatives; we
simply simulate different combinations of race-neutral practices. As noted above, the key difference
between Mr. Kahlenberg’s approach and mine is that I simulate the effects of increased consideration
of a wider set of socioeconomic attributes (including neighborhood median income and high school
median income, among others) and allow the admissions advantage received by each applicant to
vary with the applicant’s particular socioeconomic characteristics. In contrast, Mr. Kahlenberg
simulates only a single form of increased socioeconomic preference—a preference for students who
are identified as “disadvantaged” by Harvard’s admissions officers.
7.4.2. Mr. Kahlenberg’s findings
298. Despite Mr. Kahlenberg’s assertion that his proposed race-neutral alternatives could
generate a racially diverse class at little cost to the quality of the student body, Mr. Kahlenberg’s
simulations show otherwise. While the combinations of race-neutral alternatives in Simulations 1 and
2 do dramatically increase Asian-American representation, they fail to produce a substantial
proportion of AHO students (see Appendix F). Under these simulations, the proportion of AfricanAmerican students would drop 30–50% below that of the current Harvard student body. In
simulations 3, 4, and 5, the tested combinations of race-neutral alternatives are estimated to yield a
greater proportion of Hispanic students than the current student body, but to fall 20–30% short of the
current proportion of African-American students.
299. Second, Mr. Kahlenberg’s preferred combination of race-neutral alternatives (simulation
4) would be expected to produce a decline of 10% or more in the proportion of admitted students
with a 1 or 2 on each of the four profile ratings (academic, extracurricular, personal, and athletic).
That is a marked decline in the excellence of the class. Indeed, all of Mr. Kahlenberg’s simulated
combinations of race-neutral alternatives would reduce the proportion of admitted students with top

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 152

personal and athletic ratings (1 or 2), and, as noted above, would still not generate a share of admitted
students who are African-American comparable to that attained by the current race-conscious regime.
300. The admitted class would also look different in other dimensions under Mr.
Kahlenberg’s simulations. The fraction of admitted students who are children of Harvard and
Radcliffe alumni would fall substantially, as would the number of admitted students who are children
of Harvard faculty and staff. The number of athletic recruits would fall to near zero in the four
simulations that turn off the preference given to athletic recruits. In addition, all of Mr. Kahlenberg’s
simulations generate a marked increase in biological sciences concentrators, at the expense of the
humanities and social sciences.
301. Mr. Kahlenberg’s simulated combinations of race-neutral alternatives would also sharply
increase the fraction of admitted students with financial need. In his preferred simulation, about 309
additional applicants would apply for financial aid, as compared to the status quo. That would
increase Harvard’s spending by about $62 million per year (assuming equal levels of aid to all four
classes on campus at a given time).220
302. In Appendix F, I replicate Mr. Kahlenberg’s other four simulations and show how his
simulated classes would differ from Harvard’s current student body on a wide range of dimensions.
Looking across these results, I find that Mr. Kahlenberg’s proposed race-neutral alternatives do a
poor job of generating racial diversity, while also coming at a cost in terms of other class
characteristics I understand Harvard values.
7.5. Conclusion
303. In this section, I have examined whether any race-neutral admissions practice, or
combination of race-neutral practices, could enable Harvard to achieve a comparably diverse student
body without lowering the quality of the admitted class (as measured by Harvard’s profile ratings and
other indicia) or changing the composition of the admitted class in other ways that I understand
matter to Harvard. My analyses suggest that using race-neutral policies to generate diversity comes at
a cost to class quality.
304. This finding is consistent with the broader academic literature, which explains that
universities attempting to achieve racial diversity without considering race will necessarily be less
able to select the highest-quality applicants than if they could consider race. It is also consistent with
220

Harvard University, “Harvard at a Glance,” available at https://www.harvard.edu/about-harvard/harvard-glance,
accessed November 16, 2017 (“More than 55 percent of Harvard College students receive scholarship aid, and the
average grant this year is $50,000. Since 2007, Harvard’s investment in financial aid has climbed by more than 75
percent, from $96.6 million to $170 million per year.”). See workpapers.
HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 153

the results of Mr. Kahlenberg’s own simulations, which show that Harvard could achieve a
comparably diverse class only at a cost to the quality of the class. 

_____________________________
David Card
December 15, 2017

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 154

8. APPENDIX A

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 155

Curriculum Vita ‐ David Card 
December 2017 
Business Address:

Department of Economics 
530 Evans Hall #3880 
University of California Berkeley 
Berkeley, CA  94720‐3880 
phone: 510‐642‐5222   fax:  510‐643‐7042 
email: [e-mail] 

Citizenship:

U.S. and Canada 

Current Positions:            

Class of 1950 Professor of Economics 
Director, Center for Labor Economics (CLE) 
Director, Econometrics Laboratory (EML) 
Director, Labor Studies Program, National Bureau of Economic Research 

Previous Positions:

Assistant Professor of Business Economics 
Graduate School of Business 
University of Chicago, 1982‐83 
Assistant Professor of Economics 
Princeton University, 1983‐87 
Professor of Economics 
Princeton University, 1987‐1997 
Visiting Professor of Economics 
Columbia University, 1990‐91 
Fellow, Center for Advanced Study in  
Behavioral Sciences, 1996‐97 
Visiting Professor of Economics 
 Princeton University, 2000‐2001 
Visiting Professor of Economics 
 Harvard University, 2008 

Education:

Queen's University (Kingston), B.A. 1978 
Princeton University, Ph.D. 1983 

Editorial Positions:    

Co‐editor American Economic Review, 2002 ‐ 2005. 
Co‐editor Econometrica, 1993‐97 
Associate Editor Journal of Labor Economics 1988‐92   

HIGHLY CONFIDENTIAL - ATTORNEYS' EYES ONLY

Page 156

Editorial Boards:

Journal of Population Economics, 2001‐ 
AEJ: Applied Economics, 2007‐ 
Quarterly Journal of Economics, 2008‐ 

Awards and Prizes:

Doctor of Laws (Honoris Causa) University of Ottawa, 2017 
Doctor of Laws (Honoris Causa) University of Guelph, 2015 
BBVA Foundation Frontiers of Knowledge Award, 2015 
J.K. Galbraith Fellow, American Academy of Political  
     and Social Science, 2013  
Frisch Medal, 2007 (for 2005 paper in Econometrica with D. Hyslop) 
UC Berkeley Distinguished Service Award, 2007 
IZA Prize in Labor Economics, 2006   
Fellow, Society of Labor Economics, 2004 
Doctor of Laws (Honoris Causa) Queen’s University (Kingston), 1999 
John Bates Clark Prize, American Economic Assoc., 1995 
Fellow, American Academy of Arts and Sciences, 1998 
Douglas Purvis Prize, 1994 
Fellow of the Econometric Society, 1992 
Manufacturers Hanover Preceptorship, Princeton Univ., 1983‐88 
Prince of Wales Prize, Queen's University, 1978 

Invited Lectures:

Condliffe Lecture, University of Canterbury, June 2014 
Arrow Lectures, Stanford University, May 2013 
Lampman Lecture, University of Wisconsin Madison, May 2013 
Woytinsky Lecture, University of Michigan, March 2012. 
Snyder Lecture, UC Santa Barbara, April 2011.  
Ely Lecture, American Economic Association, January 2009 
Woodward Lecturer, University British Columbia, March 2008. 
Dennis Sargan Lecture to Royal Economic Society, 2006. 
Adam Smith Lecture to European Labor Economics Association, 2006. 
Fisher‐Schultz Lecture to Econometric Society, 2002. 
Alfred Marshall Lecture, Cambridge University, 2000. 

Advisory Boards:    

National Academy of Science Committee on Nat. Statistics (2012‐2015) 
“What Works Clearinghouse” Expert Panel Review (Chair),  

US Department of Education, October 2008. 
AEA Representative to US Census Advisory Committee, 1991‐96
Statistics Canada Advisory Committee, 1990‐2002 
Advisory Council, ICPSR, 1994‐96. 
Joint Center for Poverty Research, 1997‐99 
National Research Council Institute of Medicine Board on  
Children, Youth and Families, 1998‐2001. 
RWI – Essen Advisory Board, 2005‐2011. 
Comitato Scientifico Labor, Laboratorio R. Revelli, 2006‐2009.  

HIGHLY CONFIDENTIAL - ATTORNEYS' EYES ONLY

Page 157

Selected Review Panels   
and Assignments:

National Institute of Health, Social Sciences, Nursing,  
Epidemiology, and Methods (SNEM) Review Panel, 1998‐2003 
Russell Sage Foundation Immigration Advisory Committee, 1999‐ 2001. 
Government of Spain Severo Ochoa Program (2014) 
Campbell Collaboration (2016) 

Professional Societies:   

Elected member of the Council, Econometric Society, 2007‐2012 
Elected President of Society of Labor Economics for 2010/11  
President of Western Economics Association for 2015/2016 
Elected Vice President of American Economic Association for 2014/2015 

Books: 
(co‐edited with Steven Raphael). Immigration, Poverty, and Socioeconomic Inequality. New York: Russell 
Sage Foundation, 2013. 
(co‐authored with Alan B. Krueger; edited by Randall Akee and Klaus Zimmerman). Wages, School 
Quality, and Employment Demand.  Oxford: Oxford University Press, 2011. 
(co‐edited with Orley Ashenfelter)  Handbook of Labor Economics (volumes 4a‐4b). Amsterdam: Elsevier, 
2011. 
(co‐edited with Alan Auerbach and John Quigley).  Poverty, the Distribution of Income, and Public Policy.  
New York: Russell Sage Foundation, 2006. 
(co‐edited with Richard Blundell and Richard B. Freeman) Seeking a Premier Economy.  Chicago: 
University of Chicago Press for NBER, 2004. 
(co‐edited with Rebecca M. Blank)  Finding Work: Jobs and Welfare Reform.  New York: Russell Sage 
Foundation, 2000. 
(co‐edited with Orley Ashenfelter)  Handbook of Labor Economics Volumes 3a‐3c. Amsterdam: Elsevier, 
1999. 
(co‐authored with Alan B. Krueger)  Myth and Measurement: The New Economics of the Minimum Wage. 
 Princeton: Princeton University Press, 1995.  Second Edition 2016. 
(co‐edited with Richard B. Freeman).  Small Differences that Matter: Labor Markets and Income 
Maintenance in Canada and the United States. Chicago: University of Chicago Press, 1993. 

HIGHLY CONFIDENTIAL - ATTORNEYS' EYES ONLY

Page 158

Journal Articles and Chapters in Books:  
(with Stefan Bender, Nicolas Bloom, John Van Reenen, and Stephani Wolter).  "Management Practices, 
Workforce Selection, and Productivity." Journal of Labor Economics Forthcoming 2018. 
 (with Jochen Kluve and Andrea Weber). “What Works? A Meta Analysis of Recent Active Labor Market 
Program Evaluations. "  Journal of the European Economic Association October 2017. 
(with Zhuan Pei, David S. Lee and Andrea Weber). “Regression Kink Design: Theory and Practice." 
Advances in Econometrics Forthcoming 2017. 
(with Ana Rute Cardoso, Joerg Heining, and Patrick Kline). "Firms and Labor Market Inequality: Evidence 
and Some Theory." Journal of Labor Economics Forthcoming 2018. 
(with Laura Giuliano). “Can Universal Screening Increase the Representation of Low Income and Minority 
Students in Gifted Education?" Proceedings of the National Academy of Science 113, November 2016. 
(with Laura Giuliano). “Can Tracking Raise the Test Scores of High‐Ability Minority Students?”  American 
Economic Review October 2016.  
(with Ana Rute Cardoso and Patrick Kline). “Bargaining, Sorting, and the Gender Wage Gap: Quantifying 
the Impact of Firms on the Relative Pay of Women.”  Quarterly Journal of Economics May 2016.  
(with David S. Lee, Zhuan Pei and Andrea Weber).  “Inference on Causal Effects in a Generalized 
Regression Kink Design.” Econometrica 83, November 2015. 
(with Andrew Johnson, Pauline Leung, Alexandre Mas and Zhuan Pei). "The Effect of Unemployment 
Benefits on the Duration of UI Receipt: New Evidence from a Regression Kink Design in Missouri: 2003‐
2013." American Economic Review 105, May 2015. 
(with Laura Giuliano). “Peer Effects and Multiple Equilibria in the Risky Behavior of Friends.” Review of 
Economics and Statistics, 95 October 2014. 
(with Stefano Della Vigna). "Page Limits on Economics Articles: Evidence from Two Journals." Journal of 
Economic Perspectives 28 Summer 2014. 
(with Franceso Devicienti and Agata Maida). “Rent Sharing, Holdup, and Wages: Evidence from Matched 
Panel Data.”  Review of Economic Studies 84,  January 2014. 
(with Jörg Heining and Patrick Kline). "Workplace Heterogeneity and the Rise of West German Wage 
Inequality." Quarterly Journal of Economics, 128 August 2013.  
(with Stefano Della Vigna). "Nine Facts About Top Journals in Economics." Journal of Economic 
Literature, March 2013. 

HIGHLY CONFIDENTIAL - ATTORNEYS' EYES ONLY

Page 159

(with Alexandre Mas, Enrico Moretti, and Emmanuel Saez). "Inequality at Work: The Effect of Peer 
Salaries on Job Satisfaction.”  American Economic Review, October 2012. 
(with Ana Rute Cardoso).  "Can Compulsory Military Service Increase Civilian Wages? Evidence from the 
Peacetime Draft in Portugal."  American Economic Journal: Applied Economics, October 2012. 
(with Christian Dustmann and Ian Preston). “Immigration, Wages, and Compositional Amenities.”  
Journal of the European Economic Association, February 2012. 
(with Pablo Ibarraran, Ferdinando Regalia, David Rosas and Yuri Soares).  “The Labor Market Impacts of 
Youth Training in the Dominican Republic: Evidence from a Randomized Evaluation.”  Journal of Labor 
Economics, April 2011. 
(with Alexandre Mas and Jesse Rothstein). “Are Mixed Neighborhoods Always Unstable?  Two‐sided and 
One‐sided Tipping.”  In Harriet Newburger, Eugenie Birch and Susan M. Wachter, editors, Neighborhood 
and Life Chances.  Philadelphia: University of Pennsylvania Press, 2011. 
(with Stefano Della Vigna and Ulrike Malmendier.  “The Role of Theory in Field Experiments”. Journal of 
Economic Perspectives, Summer 2011. 
“Origins of the Unemployment Rate: The Lasting Legacy of Measurement without Theory.” American 
Economic Review, May 2011. 
(with Gordon B. Dahl). “Family Violence and Football: The Effect of Unexpected Emotional Cues on 
Violent Behavior.” Quarterly Journal of Economics, March 2011.  
(with Michael Ransom).  “Pension Plan Characteristics and Framing Effects in Employee Savings 
Behavior.” Review of Economics and Statistics, January 2011. 
(with Jochen Kluve and Andrea Weber). “Active Labor Market Policy Evaluations: A Meta‐Analysis.”  
Economic Journal Features, November 2010. 
(with A. Abigail Payne and Martin Dooley). “School Competition and Efficiency with Publicly‐Funded 
Catholic Schools.”  American Economic Journal: Applied Economics, 2 (October 2010). 
(with Kevin Hallock and Enrico Moretti). “The Geography of Giving: The Effect of Corporate Headquarters 
on Local Charities.”  Journal of Public Economics, 94 (April 2010). 
 (with Dean Hyslop).  “The Dynamic Effects of an Earnings Subsidy for Long‐term Welfare Recipients: 
Evidence from the SSP Applicant Experiment.”  Journal of Econometrics 153 (November 2009).  
(with Carlos Dobkin and Nicole Maestas).  “Does Medicare Save Lives?” Quarterly Journal of Economics, 
124 (May 2009). 
“Immigration and Inequality”.  American Economic Review 99 May 2009. 

HIGHLY CONFIDENTIAL - ATTORNEYS' EYES ONLY

Page 160

(with Brian McCall).  "When to Start a Fight and When to Fight Back: Workers' Compensation Liability 
Denials and Disputes."  Journal of Labor Economics, 27 (April 2009). 
“How Immigration Affects U.S. Cities.”  In Robert Inman, editor Urban Enigma: City Problems, City 
Prospects.  Princeton NJ: Princeton University Press, 2009.     
(with Carlos Dobkin and Nicole Maestas).  “The Impact of Nearly Universal Insurance Coverage on Health 
Care Utilization: Evidence from Medicare”.  American Economic Review, 98 December 2008. 
(with Rebecca Blank). “The Changing Incidence and Severity of Poverty Spells Among Female‐Headed 
Families.”  American Economic Review 98 (May 2008). 
(with Alexandre Mas and Jesse Rothstein). “Tipping and the Dynamics of Segregation.”  Quarterly Journal 
of Economics, 123 (February 2008). 
(with David S. Lee). “Regression Discontinuity Inference with Specification Error.”  Journal of 
Econometrics, 142 (February 2008). 
(with Jesse Rothstein).  “Racial Segregation and the Black‐White Test Score Gap.”  Journal of Public 
Economics, 91 (December 2007). 
(with Raj Chetty and Andrea Weber). “Cash‐on‐Hand and Competing Models of Intertemporal Behavior: 
New Evidence from the Labor Market.”  Quarterly Journal of Economics, 122 (November 2007). 
(with Enrico Moretti). “Does Voting Technology Affect Election Outcomes?  Touch‐Screen Voting and the 
2004 Presidential Election.”  Review of Economics and Statistics, 89 (November 2007). 
(with Raj Chetty and Andrea Weber). “The Spike at Benefit Exhaustion: Leaving the Unemployment 
System or Starting a New Job?”  American Economic Review,  97 (May 2007). 
(with Ethan G. Lewis).  “The Diffusion of Mexican Immigrants During the 1990s: Explanations and 
Impacts.” In George Borjas, editor, Mexican Immigration to the United States.  University of Chicago 
Press, 2007. 
(with Sara de la Rica).  “The Effect of Firm‐Level Contracts on the Structure of Wages: Evidence from 
Matched Employer‐Employee Data.”  Industrial and Labor Relations Review, October 2006.  
“Is the New Immigration Really So Bad? “ Economic Journal 115 (November 2005).  
(with Dean R. Hyslop).  “Estimating the Effects of a Time‐Limited Earnings Subsidy for Welfare Leavers.” 
Econometrica, 73 (November 2005). 
(with Alan B. Krueger).  “Would the Elimination of Affirmative Action Affect Highly Qualified Minority 
Applicants?  Evidence from California and Texas.”  Industrial and Labor Relations Review 58 (April 2005). 

HIGHLY CONFIDENTIAL - ATTORNEYS' EYES ONLY

Page 161

(with Philip Robins).  "How Important Are Entry Effects in Financial Incentive Programs for Welfare 
Recipients?  Experimental Evidence from the Self‐Sufficiency Project."  Journal of Econometrics 125 
(March‐April 2005). 
(with Charles Michalopoulos and Philip K. Robins).  “When Financial Incentives Pay for Themselves: 
Evidence from a Randomized Social Experiment for Welfare Recipients.”  Journal of Public Economics 89 
(January 2005). 
(with Lara D. Shore‐Sheppard).  “Using Discontinuous Eligibility Rules to Identify the Effects of the 
Federal Medicaid Expansions.” Review of Economics and Statistics, 86 (August 2004). 
(with Andrew K. G. Hildreth and Lara Shore‐Sheppard). “The Measurement of Medicaid Coverage in the 
SIPP: Evidence from California, 1990‐1996.”   Journal of Business and Economic Statistics, 22 (October 
2004). 
(with Thomas Lemieux and W. Craig Riddell).  “Unions and Wage Inequality.”  Journal of Labor Research, 
25 (Fall 2004).  Reprinted in James T. Bennett and Bruce E. Kaufman, editors, What Do Unions Do? A 
Twenty Year Retrospective.  New Brunswick NJ: Transaction Publishers, 2007. 
(with Richard B. Freeman).  “What Have Two Decades of British Economic Reform Delivered?”  In Richard 
Blundell, David Card, and Richard B. Freeman, editors, Seeking a Premier League Economy.  Chicago: 
University of Chicago Press for NBER, 2004 
“Canadian Emigration to the United States.”  In Charles Beach, editor, Canadian Immigration Policy for 
the 21st Century.  Kingston, Ontario: John Deutsch Institute for the Study of Economic Policy, 2003. 
(with Thomas Lemieux and W. Craig Riddell).  “Unions and the Wage Structure.”  In John T. Addison and 
Claus Schnabel, editors, The International Handbook of Trade Unions.  Cheltenham, UK: Edward Elgar, 
2003. 
(with John E. DiNardo).  “Skill Biased Technical Change and Rising Wage Inequality: Some Problems and 
Puzzles.”  Journal of Labor Economics 20 (October 2002). 
(with Orley Ashenfelter).  “Did the Elimination of Mandatory Retirement Affect Faculty Retirement 
Flows?”  American Economic Review 92 (September 2002). 
(with A. Abigail Payne).  "School Finance Reform, the Distribution of School Spending, and the 
Distribution of SAT Scores."  Journal of Public Economics 83 (January 2002). 
(with Thomas Lemieux).  "Education, Earnings, and the Canadian G.I. Bill." Canadian Journal of 
Economics 34 (May 2001). 
“Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems.”  
Econometrica 69 (September 2001). 

HIGHLY CONFIDENTIAL - ATTORNEYS' EYES ONLY

Page 162

(with Thomas Lemieux).  “Can Falling Supply Explain the Rising Return to College for Younger Men?  A 
Cohort‐Based Analysis.”  Quarterly Journal of Economics 116 (May 2001). 
(with Thomas Lemieux). “Going to College to Avoid the Draft: The Unintended Legacy of the Vietnam 
War.”  American Economic Review 91 (May 2001). 
"Immigrant Inflows, Native Outflows and the Local Labor Market Impacts of Higher Immigration."  
Journal of Labor Economics 19 (January 2001). 
"The Effect of Unions on Wage Inequality in the U.S. Labor Market."  Industrial and Labor Relations 
Review 54 (January 2001). 
“Welfare Reform and the Labor Market Outcomes of Women.”  In Paul Ong and James R. Lincoln, 
editors, The State of California Labor.  Berkeley CA: Institute of Industrial Relations, 2001. 
(with Thomas Lemieux).  “Dropout and Enrollment Trends in the Post‐War Period: What Went Wrong in 
the 1970s?”  In Jonathan Gruber, editor, Risky Behavior Among Youth: An Economic Analysis.  Chicago: 
University of Chicago Press,  2000.  
(with Alan Krueger).  “A Re‐analysis of the Effect of the New Jersey Minimum Wage with Representative 
Payroll Data.”  American Economic Review 90 (December 2000).  
(with John E. DiNardo).  “Do Immigrant Inflows Lead to Native Outflows?”  American Economic Review 
90 (May 2000). 
(with Rebecca M. Blank and Philip K. Robins).  “Financial Incentives for Increasing Work and Income 
Among Low‐Income Families.”  In Rebecca M.. Blank and David Card, editors, Finding Work: Jobs and 
Welfare Reform.  New York: Russell Sage Foundation, 2000. 
(with Phillip Levine).  "Extended Benefits and the Duration of UI Spells: Evidence from the New Jersey 
Extended Benefit Program."   Journal of Public Economics 78 (October 2000). 
(with John E. DiNardo and Eugena Estes).  "The More Things Change: Immigrants and the Children of 
Immigrants in the 1940s, the 1970s, and the 1990s."  In George J. Borjas, editor, Issues in the Economics 
of Immigration.  Chicago: University of Chicago Press, 2000. 
"The Causal Effect of Education on Earnings".  In Orley Ashenfelter and David Card, editors, Handbook of 
Labor Economics Volume 3A.  Amsterdam: Elsevier, 1999. 
(with Francis Kramarz and Thomas Lemieux).  "Changes in the Relative Structure of Wages and 
Employment: A Comparison of the United States, Canada, and France." Canadian Journal of Economics 
32 (August 1999). 
(with Thomas Lemieux).  "Adapting to Circumstances: The Evolution of Work, School, and Living 

HIGHLY CONFIDENTIAL - ATTORNEYS' EYES ONLY

Page 163

Arrangements Among North American Youth."  In David Blanchflower and Richard Freeman, editors, 
Youth Employment and Joblessness in Advanced Countries.  Chicago: University of Chicago Press, 1999. 
(with Philip Robins). "Do Financial Incentives Encourage Welfare Recipients to Work? Evidence from a 
Randomized Evaluation of the Self‐Sufficiency Project".  In Solomon Polachek, editor,  Research in Labor 
Economics  vol. 17.  Greenwich Connecticut: JAI Press, 1998. 
(with Alan Krueger).  "School Resources and Student Outcomes."  Annals of the American Academy of 
Political and Social Sciences 559 (September 1998). 
(with Thomas Lemieux). “Recent Trends in the Economic Status of North American Youth”.   Annual 
Proceedings of the Industrial Relations Research Association (December 1997). 
"Deregulation and Labor Earnings in the Airline Industry."  In James Peoples, editor, Regulatory Reform 
and Labor Markets.  Norwell, MA: Kluwer Academic Publishers, 1997. 
(with W. Craig Riddell).  "Unemployment in Canada and the United States: A Further Analysis".  In  B. 
Curtis Eaton and Richard Harris, editors, Trade, Technology, and Economics: Essays in Honour of Richard 
G. Lipsey.  Brookfield MA: Edward Elgar, 1997.
(with Dean Hyslop). "Does Inflation 'Grease the Wheels of the Labor Market'?"  In Christina D. Romer 
and David H. Romer, editors, Reducing Inflation: Motivation and Strategy.  University of Chicago Press, 
1997. 
(with Alan Krueger).  "School Resources and Student Outcomes: An Overview of the Literature and New 
Evidence from North and South Carolina".  Journal of Economic Perspectives 10 (Fall 1996). 
(with Thomas Lemieux).  "Wage Dispersion, Returns to Skill, and Black‐White Wage Differentials".  
Journal of Econometrics 74 (October 1996). 
(with Alan Krueger). "Labor Market Effects of School Quality: Theory and Evidence". 
In Gary Burtless, editor, The Link Between Schools, Student Achievement, and Adult Success.  Washington 
D.C.: Brookings Institution, 1996.
(with Brian McCall).  "Is Workers' Compensation Covering Uninsured Medical Costs? Evidence from the 
'Monday Effect'".  Industrial and Labor Relations Review 49 (July 1996). 
"The Effect of Unions on the Structure of Wages: A Longitudinal Analysis."  Econometrica 64 (July 1996). 
"Earnings, Schooling, and Ability Revisited."  In Solomon Polachek, editor, Research in Labor Economics, 
vol. 14.  Greenwich Connecticut: JAI Press, 1995. 
(with Alan Krueger).  "The Economic Return to School Quality: A Partial Survey."  In William Baumol and 
William E. Becker, editors, Assessing Educational Practices: The Contribution of Economics.  Cambridge, 
Massachusetts: MIT Press, 1995. 

HIGHLY CONFIDENTIAL - ATTORNEYS' EYES ONLY

Page 164

"Using Geographic Variation in College Proximity to Estimate the Return to Schooling".  In L.N. 
Christofides, E.K. Grant, and R. Swidinsky, editors, Aspects of Labor Market Behaviour: Essays in Honour 
of John Vanderkamp.  Toronto: University of Toronto Press, 1995. 
(with Alan Krueger).  "Time‐Series Minimum Wage Studies: A Meta‐Analysis."  American Economic 
Review 85 (May 1995).  
(with Craig Olson).  "Bargaining Power, Strike Durations, and Wage Outcomes: An Analysis of Strikes in 
the 1880s."   Journal of Labor Economics 13 (January 1995). 
(with Alan Krueger).  "Minimum Wages and Employment: A Case Study of the Fast Food Industry in New 
Jersey and Pennsylvania."  American Economic Review 84 (September 1994).  
(with Richard Freeman).  "Small Differences that Matter: Canada Versus the United States."  In Richard B. 
Freeman, editor, Working Under Different Rules.  New York: Russell Sage Foundation, 1994.  
(with Thomas Lemieux).  "Changing Wage Structure and Black‐White Wage Differentials."  American 
Economic Review 84 (May 1994). 
"Intertemporal Labor Supply: An Assessment."  In Christopher Sims, editor, Advances in Econometrics, 
Sixth World Congress. New York: Cambridge University Press, 1994. 
(with Phillip Levine). "Unemployment Insurance Taxes and the Cyclical Properties of Employment and 
Unemployment."  Journal of Public Economics 53 (February 1994). 
(with Rebecca Blank).  "Poverty, Income, and Growth: Are They Still Connected?"  Brookings Papers on 
Economic Activity, 2 (Fall) 1993. 
(with W. Craig Riddell). "A Comparative Analysis of Unemployment in the United States and 
Canada."  In David Card and Richard B. Freeman, editors, Small Differences that Matter: Labor Markets 
and Income Maintenance in Canada and the United States.  Chicago: University of Chicago Press, 1993. 
(with Alan Krueger). "Trends in Relative Black‐White Earnings Revisited."  American Economic Review 83 
(May 1993). 
"Do Minimum Wages Reduce Employment?  A Case Study of California, 1987‐89." Industrial and Labor 
Relations Review 46 (October 1992). 
"Using Regional Variation in Wages to Measure the Effects of the Federal Minimum Wage." Industrial 
and Labor Relations Review 46 (October 1992). 
(with Alan Krueger). "Does School Quality Matter: Returns to Education and the Characteristics of Public 
Schools in the United States."  Journal of Political Economy 100 (February 1992). 
(with Alan Krueger). "School Quality and Black‐White Relative Earnings: A Direct Assessment."  Quarterly 
Journal of Economics 107 (February 1992). 

HIGHLY CONFIDENTIAL - ATTORNEYS' EYES ONLY

Page 165

(with Rebecca Blank).  "Recent Trends in Insured and Uninsured Unemployment: Is There An 
Explanation?"  Quarterly Journal of Economics 106 (November 1991). 
(with Kristin Butcher). "Immigration and Wages: Evidence from the 1980s."  American Economic Review 
81 (May 1991). 
(with Joseph Altonji).  "The Effects of Immigration on the Labor Market Outcomes of Less‐Skilled 
Natives."  In John Abowd and Richard B. Freeman, editors., Immigration, Trade and Labor. Chicago: 
University of Chicago Press, 1991.   
"Minimum Wages and the Teenage Labor Market: A Case Study of California, 1987‐89."  Annual 
Proceedings of the Industrial Relations Research Association (December 1990). 
"Labor Supply with a Minimum Hours Threshold."  Carnegie Rochester Conference on Public Policy 33 
(Autumn 1990). 
"Strikes and Wages: A Test of An Asymmetric Information Model." Quarterly Journal of Economics 105 
(August 1990). 
"Unexpected Inflation, Real Wages, and Employment Determination in Union Contracts."  American 
Economic Review 80 (September 1990). 
"Strikes and Bargaining: A Survey of the Recent Empirical Literature."  American Economic Review 80 
(May 1990). 
"The Impact of the Mariel Boatlift on the Miami Labor Market."   Industrial and Labor Relations Review 
43 (January 1990). 
(with John Abowd).  "On the Covariance Structure of Earnings and Hours Changes."  Econometrica 57 
(March 1989). 
"Longitudinal Analysis of Strike Activity."  Journal of Labor Economics 6 (April 1988). 
(with Daniel Sullivan).  "Measuring the Effect of Subsidized Training Programs on Movements In and Out 
of Employment."  Econometrica 56 (May 1988). 
(with John Abowd).  "Intertemporal Labor Supply and Long Term Employment Contracts."  American 
Economic Review 77 (March 1987). 
"Efficient Contracts with Costly Adjustment:  Short Run Employment Determination for Airline 
Mechanics."  American Economic Review 76 (December 1986). 
"The Impact of Deregulation on the Employment and Wages of Airline Mechanics."  Industrial and Labor 
Relations Review 39 (July 1986). 
(with Orley Ashenfelter).  "Why Have Unemployment Rates in Canada and the United States Diverged?"  

HIGHLY CONFIDENTIAL - ATTORNEYS' EYES ONLY

Page 166

Economica 53 (Supplement 1986). 
"An Empirical Model of Wage Indexation Provisions in Union Contracts."  Journal of Political Economy 94 
(June 1986). 
(with Orley Ashenfelter).  "Using the Longitudinal Structure of Earnings to Estimate the Effect of Training 
Programs."  Review of Economics and Statistics 67 (November 1985). 
"Microeconomic Models of Wage Indexation."  Annual Proceedings of the Industrial Relations Research 
Association (December 1984). 
"Cost of Living Escalators in Major Union Contracts."  Industrial and Labor Relations Review, 37 (October 
1983). 
(with Orley Ashenfelter).  "Time Series Representations of Economic Variables and Alternative Models of 
the Labor Market."  Review of Economic Studies, 69 (September 1982). 

Unpublished Papers:   
(with Stefano Della Vigna). "What Do Editors Maximize? Evidence from Four Leading Economics 
Journals." NBER Working Paper Number 23282, March 2017. 
(with Nicole Maestas and Patrick Purcell). “Labor Market Shocks and Early Social Security Benefit 
Claiming.” Working Paper 2014‐317. Michigan Retirement Research Center. 
(with Zhuan Pei, David S. Lee and Andrea Weber). “Local Polynomial Order in Regression Discontinuity 
Designs.”  Working Paper 2014‐47.  Brandeis University Department of Economics. October 2014. 
(with Laura Giuliano). "Does Gifted Education Work?  For Which Students?" NBER Working Paper 
Number 204553, September 2014. 
(with David S. Lee, Zhuan Pei, and Andrea Weber). “Infererence on Causal Effects in a Generalized 
Regression Kink Design."  IZA Working Paper 8757, January 2015.   
(with Pablo Ibarrarán and Juan Miguel Villa) Miguel. "Building in an Evaluation Component for Active 
Labor Market Programs: A Practitioner's Guide."   IZA Discussion Paper 6085, 2011. 
(with Thomas Lemieux).  “Did Draft Avoidance Raise College Attendance During the Vietnam War?” UC 
Berkeley Center for Labor Economics Working Paper 46, February 2002. 
(with Philip K. Robins and Charles Michalopoulos).  “The Limits to Wage Growth: Measuring The Growth 
Rate of Wages for Recent Welfare Leavers”.  NBER Working Paper No. 8444, August 2001.  
“Reforming the Financial Incentives of the Welfare System”.  IZA Discussion Paper No. 172.  Institute for 

HIGHLY CONFIDENTIAL - ATTORNEYS' EYES ONLY

Page 167

the Study of Labor (IZA), Bonn Germany. 
"The Effect of Unions on the Level and Distribution of Wages: A Longitudinal Analysis."  Princeton 
University Industrial Relations Section Working Paper Number 287, July 1991, revised March 1995. 
"Supply and Demand in the Labor Market."  Princeton University Industrial Relations Section Working 
Paper Number 228, November 1987. 
(with Orley Ashenfelter).  "Using Longitudinal Data to Measure Minimum Wage Effects."  Center for 
Labour Economics, London School of Economics Discussion Paper, September 1981. 
Published Reviews and Comments: 
(with Orley Ashenfelter). "Introduction to Special Issue in Honor of Robert J. Lalonde" Journal of Labor 
Economics , forthcoming 2017. 
(with Giovanni Peri). “Immigration Economics by George J. Borjas – A Review”.  Journal of Economic 
Literature, December 2016. 
(with Alexandre Mas). "Introduction to Special Issue on Labor Markets in the Great Recession." Journal 
of Labor Economics 34, January 2016. 
L'évaluation des Politiques Actives du Marché du Travail: Quels Enseignements?  Travail et Emploi 139, 
Juillet‐Septembre 2014. 
"The Elusive Search for Negative Wage Impacts of Immigration" Journal of the European Economic 
Association, February 2012. 
Comment on Robert A. Moffitt, “Demographic Change and Public Assistance Expenditures.”  In Alan 
Auerbach, editor, Demographic Change and Fiscal Policy.  New York: Cambridge University Press, 2000. 
“The Research Contributions of Thomas Lemieux.”  Canadian Journal of Economics 31 (October 1998): 
975‐984. 
"The Wage Curve: A Review."  Journal of Economic Literature 33 (June 1995). 
(with Lawrence Katz and Alan Krueger).  Comment on David Neumark and William Wascher, 
"Employment Effects of Minimum and Subminimum Wages: Panel Data on State Minimum Wage Laws". 
 Industrial and Labor Relations Review 47 (April 1994). 
Review of Michael Goldfield's The Decline of Organized Labor in the United States.  Journal of Economic 
History 49 (December 1989). 
Review of H. Gregg Lewis' Union Relative Wage Effects: A Survey.  Journal of Economic Literature, 25 
(March 1987). 

HIGHLY CONFIDENTIAL - ATTORNEYS' EYES ONLY

Page 168

(with Henry Farber).  "Comments on Semi‐Parametric Estimation of Employment Duration Models," by 
Joel Horowitz and George Neumann.  Econometric Reviews, (1988). 
Comments on "Empirical Tests of Labor Market Equilibrium: An Evaluation," by J. Heckman and T. 
MaCurdy. Carnegie Rochester Conference Series on Public Policy, 28 (Spring 1988). 
Comments on "Macroeconomic Performance and the Disadvantaged," by Lawrence Katz and David 
Cutler.  Brookings Papers on Economic Activity Number 2, 1991.  

HIGHLY CONFIDENTIAL - ATTORNEYS' EYES ONLY

Page 169

Reports and Testimony of Prof. David Card
in the Last Four Years
Federal Home Loan Bank of Seattle v. Barclays Capital, Inc., et al. (Case No. 09-2-46320-4
SEA), Superior Court of Washington for King County: report, February 21, 2014; deposition,
February 13, 2015.
MBIA Insurance Corporation v. Credit Suisse Securities (USA) LLC, DLJ Mortgage Capital,
Inc., and Select Portfolio Servicing, Inc. (No. 603751/09), Supreme Court of the State of New
York for the County of New York: report, September 30, 2015; deposition, February 25, 2016.
Home Equity Mortgage Trust Series 2006-1, Home Equity Mortgage Trust Series 2006-3, and
Home Equity Mortgage Trust Series 2006-4 vs DLJ Mortgage Capital Inc. and Select Portfolio
Servicing, Inc. (Index number 156016/2012) and Home Equity Mortgage Trust Series 2006-5 by
US Bank National Association solely in its capacity as Trustee vs DLJ Mortgage Capital Inc.
and Select Portfolio Servicing, Inc. (Index number 653787/2012), Supreme Court of the State of
New York for the County of New York: report, October 14, 2016; deposition April 18, 2017.
US Bank National Association solely in its capacity as Trustee of the Home Equity Asset Trust
2007-1 vs DLJ Mortgage Capital Inc. (Index number 650369/2013) Supreme Court of the State
of New York for the County of New York: report, December 2, 2016; deposition April 19, 2017.

HIGHLY CONFIDENTIAL - ATTORNEYS' EYES ONLY

Page 170

9. APPENDIX B
9.1. Documents relied upon
Expert Reports
Expert Report of Peter S. Arcidiacono and backup materials, Students for Fair Admissions, Inc. v.
President and Fellows of Harvard College (Harvard Corporation), October 16, 2017.
Expert Report of Richard D. Kahlenberg and backup materials, Students for Fair Admissions, Inc.
v. President and Fellows of Harvard College (Harvard Corporation), October 16, 2017.
Harvard Admissions Data
HARV00001203 – 53; HARV00001373 – 80; HARV00006413 – 6818; NEVO Admissions Data
for the classes of 2014 – 2019.
HARV00001224 and HARV00001322, Lists of database fields produced.
HARV00001895; HARV00001985; HARV00002725 – 29; HARV00003489, Documents
pertaining to calculation of Academic Index.
Depositions
Deposition of Brock Walsh, June 28, 2017.
Deposition of Caroline A. Weaver, Volume I, October 9, 2015.
Deposition of Caroline A. Weaver, Volume II, March 6, 2017.
Deposition of Catherine Drew Gilpin Faust, March 10, 2017.
Deposition of Chris Looby, June 30, 2017.
Deposition of Elizabeth Yong, March 24, 2017.
Deposition of Erica Bever, July 13, 2017.
Deposition of Grace Cheng, April 7, 2014.
Deposition of Kaitlin Howrigan, June 20, 2017.
Deposition of Lucerito Ortiz, June 14, 2017.
Deposition of Marlyn McGrath, Volume I, June 18, 2015.
Deposition of Marlyn McGrath, Volume II, August 1, 2017.
Deposition of Rakesh Khurana, March 27, 2017.
Deposition of Roger Banks, May 5, 2017.
Deposition of Sarah Donahue, June 6, 2017.
Deposition of Tia Ray, June 7, 2017.
Deposition of William Fitzsimmons, August 3, 2017.
Academic Articles
Aaron Danielson and Richard H. Sander, “Thinking Hard About ‘Race-Neutral’ Admissions,”
University of Michigan Journal of Law Reform 47(4), 2014, pp. 967–1020.
Amanda Griffith and Donna Rothstein, “Can’t Get Here from There: The Decision to Apply to a
Selective Institution,” Economics of Education Review 28(5), 2009, pp. 620–628.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 171

Caroline Hoxby and Christopher Avery, “The Missing “One-Offs”: The Hidden Supply of HighAchieving, Low-Income students,” Brookings Papers on Economic Activity 2013(1), pp. 1–65.
Christopher Avery and Jonathan Levin, “Early Admission at Selective Colleges,” Stanford
Institute for Economic Policy Research No. 08–31, March 2009, pp. 1–38.
David Card and Alan Krueger, “Would the Elimination of Affirmative Action Effect Highly
Qualified Minority Applicants? Evidence from California and Texas,” Industrial and Labor
Relations Review 58(3), 2005, pp. 416–434.
Daniel Koretz, Michael Russell, Chingwei David Shin, Cathy Horn, and Kelly Shasby, “Testing
and diversity in postsecondary education: The case of California,” Education Policy Analysis
Archives10(1), 2002, pp. 1–39.
Glenn Ellison and Parag Pathak, “The Efficiency of Race-Neutral Alternatives to Race-Based
Affirmative Action: Evidence from Chicago’s Exam Schools,” NBER Working Paper w22589,
2016, pp. 1–59.
Jimmy Chan and Erik Eyster, “Does Banning Affirmative Action Lower College Student
Quality?,” American Economic Review 93(3), 2003, pp. 858–872.
Julie J. Park and M. Kevin Eagan, “Who Goes Early? A Multi-Level Analysis of Enrolling via
Early Action and Early Decision Admissions,” Teachers College Record, 2011, pp. 2345–2373.
Mark Long, “Is There a ‘Workable’ Race-Neutral Alternative to Affirmative Action in College
Admissions?” Journal of Policy Analysis and Management 34(1), 2015, pp. 162–183.
Mark Long, “The Promise and Peril for Universities Using Correlates of Race in Admissions in
Response to the Grutter and Fisher Decisions,” ETS White Paper, 2015, pp. 1–35.
Peter Hinrichs, “The effects of affirmative action bans on college enrollment, educational
attainment, and the demographic composition of universities,” The Review of Economics and
Statistics 94(3), 2012, pp. 712–722.
Roland Fryer and Glenn Loury, “Affirmative Action and Its Mythology,” The Journal of
Economic Perspectives 19(3), 2005, pp. 147-162.
Roland Fryer, Glenn Loury, and Tolga Yuret, “An Economic Analysis of Color-Blind Affirmative
Action,” The Journal of Law, Economics, & Organization 24(2), 2007, pp. 319–355.
Sandra Black, Kalena Cortes, and Jane Lincove, “Apply Yourself: Racial and Ethnic Difference in
College Application,” NBER Working Paper #21368, 2015.
Sandra Black, Kalena Cortes, and Jane Lincove, “Academic Undermatching of High-Achieving
Minority Students: Evidence from Race-Neutral and Holistic Admissions Policies.” American
Economic Review: Papers & Proceedings 105(5), 2015, pp. 604–610.
Saul Geiser and Maria Veronica Santelices, “Validity of High-School Grades in Predicting
Student Success beyond the Freshman Year: High-School Record vs. Standardized Tests as
Indicators of Four-Year College Outcomes,” Research & Occasional Paper Series CSHE 6.07,
2007, pp. 1–35.
Sean Reardon, Rachel Baker, and Daniel Klasik, “Race, income, and enrollment patterns in highly
selective colleges, 1982-2004,” Center for Education Policy Analysis, Stanford University, 2012,
pp. 1–27.
Sharmila Choudhury, “Reassessing the Male-Female Wage Differential: A Fixed Effects
Approach,” Southern Economic Journal 60(2), 1993, pp. 327–340.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 172

Thomas Espenshade and Chang Y. Chung, “The opportunity cost of admission preferences at elite
universities,” Social Science Quarterly 86(2), 2005, pp. 293–305.
Thomas J. Kane, “Racial and Ethnic Preferences in College Admissions,” Ohio St. Law Journal
59, 1998, pp. 971–996.
Books and Book Chapters
A. Colin Cameron and Pravin K. Trivedi, Microeconometrics: Methods and Applications
(Cambridge University Press, 2009), pp. 467 and 501.
Anthony P. Carnevale and Stephen J. Rose, “Socioeconomic Status, Race/Ethnicity. And Selective
College Admissions,” in America’s Untapped Resource: Low Income Students, ed. Richard
Kahlenberg (Century Foundation Press, 2004), pp. 101–156.
Anthony P. Carnevale, Stephen J. Rose, and Jeff Strohl, “Achieving Racial and Economic
Diversity with Race-Blind Admissions Policy,” in The Future of Affirmative Action, ed. Richard
Kahlenberg (Century Foundation Press, 2014), pp. 187–202.
Daniel L. Rubinfeld, “Reference Guide on Multiple Regression,” in Reference Manual on
Scientific Evidence: Third Edition (The National Academies Press, 2011), pp. 305–307.
Halley Potter, “Transitioning to Race-Neutral Admissions: An Overview of Experiences in States
Where Affirmative Action Has Been Banned,” in The Future of Affirmative Action, ed. Richard
Kahlenberg (Century Foundation Press, 2014), pp. 75–90.
James H. Stock and Mark W. Watson, Introduction to Econometrics (Pearson, 2015), pp.183–184,
189 and 232–234.
John Brittain and Benjamin Landy, “Reducing Reliance on Testing to Promote Diversity,” in The
Future of Affirmative Action, ed. Richard Kahlenberg (Century Foundation Press, 2014), pp. 160–
174.
Kenneth E. Train, “Properties of Discrete Choice Models,” in Discrete Choice Methods with
Simulation (The Cambridge University Press, 2009), p. 34.
Matthew N. Gaertner, “Advancing College Access with Class-Based Affirmative Action,” in The
Future of Affirmative Action, ed. Richard Kahlenberg (Century Foundation Press, 2014), pp. 175–
186.
Sigal Alon, Race, Class, and Affirmative Action (Russell Sage Foundation, 2015), pp. 254–256. 
William H. Greene, “Models for Discrete Choice,” in Econometric Analysis (Pearson, 2008), pp.
8–10, 773–774.
Public Press/Websites
“Harvard at a Glance,” available at https://www.harvard.edu/about-harvard/harvard-glance,
accessed November 16, 2017.
“Resources for Implementing Changes to Race/Ethnicity Reporting in IPEDS,” National Center
for Education Statistics, available at https://nces.ed.gov/ipeds/Section/Resources.
“Harvard in the Community,” 2016, available at
https://hwpi.harvard.edu/files/comm/files/2016_cambridge_impact_mailing.pdf, accessed
November 27, 2017.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 173

Brown University, “General Questions,” available at
https://www.brown.edu/about/administration/financial-aid/general-questions, accessed December
5, 2017.
Cambridge Rindge and Latin School, “Support & Enrichment Programs,” available at
http://crls.cpsd.us/academics/support___enrichment_programs, accessed November 27, 2017.
Cornell University, “Financial Aid, Financial Aid Initiatives,” available at
https://finaid.cornell.edu/cost-attend/financial-aid-initiatives, accessed December 5, 2017.
Dartmouth College, “How Aid Works,” available at http://admissions.dartmouth.edu/financialaid/how-aid-works/how-much-help-will-i-get, accessed December 5, 2017.
Harvard College, “Harvard College Connection,” available at
https://college.harvard.edu/admissions/hear-our-students/harvard-college-connection, accessed
December 12, 2017.
Harvard College, “Restrictive Early Action,” available at
https://college.harvard.edu/admissions/apply/application-timeline/restrictive-early-action, accessed
August 14, 2017.
Harvard College, “What is Harvard’s graduation rate?,” available at
https://college.harvard.edu/what-harvards-graduation-rate, accessed December 5, 2017.
Harvard College, Financial Aid Fact Sheets, available at https://college.harvard.edu/financial-aid/howaid-works/fact-sheet, accessed December 14, 2017.
School of Engineering and Applied Sciences, “Timeline,” available at
https://www.seas.harvard.edu/about-seas/history-seas/timeline, accessed November 20, 2017.
New York Times, “Top Colleges Doing the Most for the American Dream,” available at
https://www.nytimes.com/interactive/2017/05/25/sunday-review/opinion-pell-table.html, accessed
November 19, 2017.
Penn Student Registration & Financial Services, “A Look at the Facts,” available at
http://www.sfs.upenn.edu/paying/paying-pro-look-at-the-facts.htm, accessed December 5, 2017.
Princeton University, “How Princeton’s Aid Program Works,” available at
https://admission.princeton.edu/cost-aid/how-princetons-aid-program-works, accessed December
5, 2017.
Stanford University, “How Aid Works,” available at
https://financialaid.stanford.edu/undergrad/how/parent.html, accessed December 5, 2017.
U.S. News and World Report, “Top 100 Lowest Acceptance Rates,” available at
https://www.usnews.com/best-colleges/rankings/lowest-acceptance-rate, accessed December 7,
2017.
Columbia University, “How Aid Works,” available at https://ccseas.financialaid.columbia.edu/how/aid/works, accessed December 5, 2017.
William Fitzsimmons, “Guidance Office: Answers From Harvard’s Dean, Part 1,” New York
Times, September 10, 2009, available at
https://thechoice.blogs.nytimes.com/2009/09/10/harvarddean-part1/, accessed November 10, 2017.
Yale University, “Financial Aid In-Depth,” available at https://admissions.yale.edu/financial-aidprospective-students, accessed December 5, 2017.
Produced Documents
HARV00000008 – 09, “Interview Information Sheet Class of 2017,”

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 174

HARV00000212 – 321, “2012 Casebook”
HARV00001322 – 61, “Database Fields Produced 11/6/2015”
HARV00001392 – 438, “Interviewer Handbook, 2014-2015”
HARV00001848 – 1850, Table, “Demographic Breakdown of Applicants, Admits, and
Matriculants”
HARV00003906, Table, Four- and six-year graduation rates
HARV00006413, Spreadsheet, 2014.xlsx (list of variables produced and withheld)
HARV00006471, Spreadsheet, 2015.xlsx (list of variables produced and withheld)
HARV00006541, Spreadsheet, 2016.xlsx (list of variables produced and withheld)
HARV00006607, Spreadsheet, 2017.xlsx (list of variables produced and withheld)
HARV00006695, Spreadsheet, 2018.xlsx (list of variables produced and withheld)
HARV00006759, Spreadsheet, 2019.xlsx (list of variables produced and withheld)
HARV00007766 – 71, Table, “Class of 2017 - EA Applicants”
HARV00008048 – 69, Harvard Report, “Report of the Committee to Study the Importance of
Student Body Diversity,” 2016
HARV00009879 – 80, Harvard Memo, “Re: Faculty Readings”, November 9, 2013
HARV00010744, Harvard Memo, “A decade of improving access and affordability at Harvard”
HARV00013561 – 65, Sarasota Presentation, “KLW - Sarasota Presentation”
HARV00015410 – 27, “Reading Procedures, Class of 2018”
HARV00016119 – 70, Office of Institutional Research Presentation, “Harvard College Financial
Aid,” June 13, 2013
HARV00018164 – 76, “Discussion Guide to the 2012 Casebook”
HARV00023177 – 8, Table, Aggregate applicant data 1980 – 2018
HARV0023564, Table, “Searches 2013 - Class of 2018”
HARV00027590 – 97, Email from Jeff A. Neal to William Fitzsimmons, “Draft Gazette Article
with Tuition/Smith,” March 24, 2014
HARV00030332 – 32, Memo from Drew Faust and Mike Smith to Members of the Corporation,
“Proposed Changes in Admission Policy,” February 2, 2011
HARV00031659 – 86, Harvard Presentation, “Materials for Financial Aid Meeting,” June 14,
2013
HARV00031687 – 772, Office of Institutional Research Presentation, “Admissions and Financial
Aid at Harvard College,” February 2013
HARV00031933, “Admissions Calendar 2013–2014”
HARV00032509 – 24, Harvard Table, “Demographic Breakdown of Applicants, Admits, and
Matriculants”
HARV00065450 – 52, Harvard Memo, “A Note on the Collection and Reporting of Data on Race
and Ethnicity”
HARV00067773 – 804, Office of Institutional Research Presentation, “Harvard College Financial
Aid: Recent Initiatives and Results,” July 25, 2013
Legal Documents
Harvard’s Objections and Responses to Plaintiff’s Second Set of Interrogatories, July 20, 2017.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 175

Complaint, Students for Fair Admissions, Inc. v. President and Fellows of Harvard College
(Harvard Corporation); and the Honorable and Reverend the Board of Overseers, November 17,
2014.
Other Data and Data Documentation
College Board, “2010 College-Bound Seniors,” Total Group Profile Report, p. 4, available at
https://research.collegeboard.org/programs/sat/data/archived/cb-seniors-2010, accessed December
14, 2017.
College Board, “2011 College-Bound Seniors,” Total Group Profile Report, p. 4, available at
https://research.collegeboard.org/programs/sat/data/archived/cb-seniors-2011, accessed December
14, 2017.
College Board, “2012 College-Bound Seniors,” Total Group Profile Report, September 24, 2012,
p. 4, available at https://research.collegeboard.org/programs/sat/data/archived/cb-seniors-2012,
accessed December 14, 2017.
College Board, “2013 College-Bound Seniors,” Total Group Profile Report, September 2013, p. 4,
available at https://research.collegeboard.org/programs/sat/data/archived/cb-seniors-2013,
accessed December 14, 2017.
College Board, “2014 College-Bound Seniors,” Total Group Profile Report, October 7, 2014, p.4,
available at https://research.collegeboard.org/programs/sat/data/archived/cb-seniors-2014,
accessed December 14, 2017.
College Board, “2015 College-Bound Seniors,” Total Group Profile Report, September 30,
2015, p. 4, available https://research.collegeboard.org/programs/sat/data/archived/cb-seniors-2015,
accessed December 14, 2017.
College Board, “Descriptor PLUS Cluster Description Guide,” available at
http://media.collegeboard.com/digitalServices/pdf/miscellaneous/ClusterDescriptionGuide.pdf,
accessed December 14, 2017.
College Board, “Segment Analysis Service Tagging/Historical Analysis Output File,” available at
http://media.collegeboard.com/mSSS/media/pdf/segment-analysis-tagging-output-filesep2013.pdf, accessed December 14, 2017.
College Board, “Segment Analysis Service: An Educationally Relevant Geodemographic Tagging
Service,” available at http://media.collegeboard.com/mSSS/media/pdf/segment-analysis-serviceoverview.pdf, accessed December 14, 2017.
College Board Segment Analysis Service, Applicant-level data (“College Board Cluster Data”).
National Center for Education Statistics, “Number of educational institutions, by level and control
of institution: Selected years, 1980-81 through 2013-14,” Digest of Education Statistics 2015,
December 2016.
United States Bureau of Labor Statistics, “2010 SOC User Guide,” February 2010, available at
https://www.bls.gov/soc/soc_2010_user_guide.pdf, accessed December 13, 2017.
United States Bureau of Labor Statistics, “2010 Standard Occupational Classification,” January
2009, available at https://www.bls.gov/soc/soc_structure_2010.pdf, accessed December 13, 2017.
United States Census Bureau, “2010 ANSI Codes for Places.”
United States Census Bureau, “2010 FIPS Codes for Counties and County Equivalent Entities.”
United States Census Bureau, “Core based statistical areas (CBSAs), metropolitan divisions, and
combined statistical areas (CSAs),” July 2015.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 176

United States Census Bureau, Current Population Survey 2017 Annual Social and Economic
Supplement, HINC-01: Selected Characteristics of Households by Total Money Income, available
at https://www.census.gov/data/tables/time-series/demo/income-poverty/cps-hinc/hinc-01.html,
accessed December 14, 2017.
United States Census Bureau, "Census regions and Divisions in the United States," available at
https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf, accessed December
14, 2017.
United States Census Bureau, “ZIP Code Tabulation Areas,” 2016 U.S. Gazetteer Files, available
at https://www.census.gov/geo/maps-data/data/gazetteer2016.html, accessed December 14, 2017.
All other documents cited in this report.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 177

10. APPENDIX C
10.1. Parent occupations

Mother’s occupation differs by race

White
16.8%
15.8%

AsianAmerican
18.5%
14.9%

AfricanAmerican
7.1%
24.2%

Hispanic
or Other
15.9%
22.3%

Race
Missing
17.1%
17.3%

10.0%

4.3%

8.0%

9.6%

7.0%

8.0%

8.0%

6.6%

4.3%

8.0%

7.4%

5.1%

6.7%

5.6%

6.5%

4.3%

0.7%

2.6%

2.2%

3.4%

4.2%

3.7%

9.6%

3.2%

3.6%

4.0%
3.7%
3.4%

6.3%
2.7%
3.9%

8.3%
4.2%
2.2%

7.6%
4.4%
3.2%

5.2%
2.5%
3.7%

11. Business and Financial Operations

3.3%

4.8%

3.6%

2.7%

3.5%

12.
13.
14.
15.
16.

3.0%
2.4%
2.4%
2.0%
1.8%

1.0%
2.1%
1.8%
4.9%
4.9%

0.9%
1.2%
1.9%
0.7%
0.8%

1.4%
1.3%
2.3%
1.0%
1.5%

2.6%
2.4%
1.7%
3.4%
3.8%

1.8%

0.7%

3.3%

1.9%

1.4%

1.7%

1.1%

1.7%

1.5%

1.0%

1.4%

6.1%

1.1%

0.8%

3.8%

1.2%

3.3%

2.9%

5.3%

1.3%

0.6%

0.9%

1.4%

1.6%

0.3%

0.5%

0.4%

0.2%

0.2%

0.4%

0.1%
0.1%

0.0%
0.0%

0.6%
0.4%

0.3%
0.1%

0.1%
0.0%

Occupation Category
1. Homemaker
2. Other
Pre-K through Grade 12 Educational
3.
Instruction & Library
Health Diagnosing and Treating
4.
Practitioners
Business Executive (management,
5.
administrator)
Lawyers, Judges and Related
6.
Workers
Other Healthcare Occupations Incl.
7.
Nurses
8. Unemployed
9. Office and Administrative Support
10. Self-Employed

17.
18.
19.
20.
21.
22.
23.
24.

Art, Design and Media
Postsecondary Teachers
Sales and Related
Life, Physical and Social Sciences
Architecture and Engineering
Counselors, Social Workers,
Community Service
Other Management (Excl. Business
Execs)
Computer and Mathematical
Skilled Trades Incl. Construction
and Extraction
Low Skill.
Entertainers, Performers and Sports
Related Workers
Protective Service
Military

Source: Augmented Arcidiacono Data
Note: Sample consists of Prof. Arcidiacono’s expanded sample for the classes of 2014 – 2019. For the class of 2018, the “Unemployed”
category is combined with the “Homemaker” category in my year-by-year models, affecting 10 observations.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 178

Father’s occupation differs by race

Occupation Category
Business Executive (management,
1.
administrator)
2. Other
Health Diagnosing and Treating
3.
Practitioners
4. Architecture and Engineering
5. Self-Employed
Lawyers, Judges and Related
6.
Workers
Skilled Trades Incl. Construction
7.
and Extraction
8. Computer and Mathematical
9. Sales and Related
10. Postsecondary Teachers

White

AsianAmerican

AfricanAmerican

Hispanic
or Other

Race
Missing

17.2%

11.8%

7.8%

11.4%

14.5%

13.9%

13.4%

31.0%

24.1%

15.5%

9.7%

9.6%

6.6%

6.1%

10.4%

9.1%
8.2%

17.7%
7.5%

5.8%
4.6%

6.8%
6.8%

13.8%
7.7%

7.7%

1.3%

2.6%

3.9%

5.4%

4.8%

4.2%

7.4%

13.6%

3.3%

4.0%
3.8%
3.1%

8.8%
1.8%
4.9%

2.7%
2.6%
1.9%

2.3%
3.5%
1.4%

6.8%
2.5%
4.1%

11. Business and Financial Operations

3.0%

2.3%

2.7%

2.2%

2.1%

12. Life, Physical and Social Sciences
Pre-K through Grade 12 Educational
13.
Instruction & Library
Other Management (Excl. Business
14.
Execs)
15. Unemployed
16. Art, Design and Media
17. Protective Service
Counselors, Social Workers,
18.
Community Service
19. Military
20. Low Skill.
21. Office and Administrative Support
Entertainers, Performers and Sports
22.
Related Workers
Other Healthcare Occupations Incl.
23.
Nurses
24. Homemaker

2.6%

6.6%

1.0%

1.2%

4.4%

2.4%

0.7%

2.4%

2.0%

1.3%

2.2%

1.3%

1.3%

1.8%

1.2%

2.2%
1.4%
0.9%

3.2%
0.5%
0.2%

8.0%
0.7%
1.9%

4.8%
0.8%
1.8%

2.7%
1.0%
0.5%

0.9%

1.0%

2.4%

0.7%

0.8%

0.9%
0.6%
0.6%

0.3%
0.8%
0.8%

1.6%
1.4%
1.2%

0.9%
1.8%
1.0%

0.3%
0.2%
0.5%

0.4%

0.2%

0.5%

0.4%

0.4%

0.4%

0.7%

1.5%

0.5%

0.4%

0.3%

0.2%

0.2%

0.3%

0.3%

Source: Augmented Arcidiacono Data
Note: Sample consists of Prof. Arcidiacono’s expanded sample for the classes of 2014 – 2019. For the class of 2018, the “Unemployed”
category is combined with the “Homemaker” category in my year-by-year models, affecting 5 observations.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 179

11. APPENDIX D
11.1. Primary activities

Primary activities differ by race

White
26%

Primary Activity
1. Varsity athletics

AsianAmerican
16%

AfricanAmerican
26%

Hispanic
or Other
24%

Race
Missing
16%

2. Community service

22%

27%

25%

27%

26%

3. Other

14%

14%

14%

14%

15%

4. JV athletics

13%

7%

12%

11%

11%

5. Instrumental music

12%

17%

9%

10%

15%

6. Academic

11%

11%

13%

13%

10%

7. Politics

10%

9%

11%

10%

9%

8. Work

9%

7%

8%

8%

8%

9. Science or math

8%

18%

5%

7%

14%

10. Speech and debate

7%

9%

6%

6%

8%

11. Club athletics

6%

4%

5%

6%

5%

12. Drama

6%

2%

4%

4%

4%

13. Journalism

5%

6%

3%

4%

6%

14. Career

5%

8%

5%

5%

8%

15. Religious

3%

3%

5%

4%

2%

16. Vocal music

3%

2%

3%

3%

2%

17. Dance

3%

3%

4%

3%

3%

18. Computer

2%

4%

2%

2%

4%

19. Foreign language

2%

2%

2%

2%

2%

20. Art

2%

2%

2%

2%

3%

21. Missing

2%

1%

6%

4%

1%

22. Environmental

1%

2%

1%

1%

1%

23. Foreign exchange

1%

1%

1%

1%

1%

24. Cultural

1%

2%

3%

2%

1%

25. Robotics

1%

1%

1%

1%

1%

26. Family

1%

1%

1%

1%

1%

27. LGBT

1%

0%

0%

1%

0%

28. School spirit

0%

0%

1%

1%

0%

29. Junior ROTC

0%

0%

1%

1%

0%

Sorce: Augmented Arcidiacono Data
Note: Data are from applicants to the classes of 2017 – 2019 in Professor Arcidiacono’s expanded sample. Primary activities consist of
activities that applicants listed as either activity 1 or activity 2. Categories for activities can vary year to year.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 180

12. APPENDIX E
12.1. Variables used in logit model of admissions

Card Model
Variable Name

Variable Description

Arcidiacono
Variable

Pooled

Yearby-year

Race Variables
Mutually exclusive race categories, based on
ethnic_group_cde field with categories: “White,”
“Black,” “Hispanic, Mexican, or Puerto Rican,” “Asian,”
“Native American,” “Hawaiian or Pacific Islander,”
“Race Missing.”
Mutually exclusive race categories, based on
ethnic_group_cde field with categories: “White,”
“Black,” “Hispanic and Other,” “Asian,” “Race Missing.”
“Other” includes Mexican, Puerto Rican, Native
American, Hawaiian, and Pacific Islander.

race

racecoll












Base Controls
year

Harvard class to which applicant applies: 2014 to 2019









female

Indicator for whether applicant indicated “Female” in a
sex code entry field









disadvantaged

Indicator for whether applicant was flagged by
admissions staff, based on application, as likely
socioeconomically disadvantaged or HFAI eligible









fgcl

Indicator for first generation college applicant









earlyDecision

Indicator for Early Action applicant









athlete

Indicator for athletic profile rating of 1









































legacy
double_legacy
faculty_or_staff_kid
deanDirectorPref

Indicator for whether at least one of applicant’s parents
attended Harvard
Indicator for whether both of applicant’s parents
attended Harvard
Indicator for whether applicant is child of Harvard
faculty or staff
Indicator for whether applicant is on Dean’s or
Director’s Interest Lists

waiver_tot

Indicator for whether applicant requested a fee waiver









finaid

Indicator for whether applicant applied for financial aid
Categories for mother’s level of education: “Less than
college,” “College graduate,” “Master’s,” “MD/JD/PhD,”
“Missing”
Categories for father’s level of education: “Less than
college,” “College graduate,” “Master’s,” “MD/JD/PhD,”
“Missing”
Categories for applicant’s intended major: “Social
sciences,” “Humanities,” “Biological sciences,” “Physical
sciences,” “Engineering,” “Mathematics,” “Computer
Sciences,” “Unspecified”
Docket to which applicant’s high school is assigned









































meduc
feduc

intendedMajor
docketFE

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 181









Card Model
Variable Name

Variable Description

Arcidiacono
Variable

Pooled

Yearby-year

Academic Variables
SACTmath_std
Normalized ACT/SAT math score







SACTverb_std

Normalized ACT/SAT verbal score









SAT2avg_std

Normalized average SAT II subject test score









gpa_converted_std

Normalized converted GPA









academic_index_std

Normalized academic index









academic_index2p

Normalized academic index quadratic multiplied by
indicator for positive normalized academic index









academic_index2m

Normalized academic index quadratic multiplied by
indicator for negative normalized academic index









flaggpa

Indicator for converted GPA equal to 35









m_SAT2avg

Indicator for missing average SAT II score




























Ratings Variables

APEA_combos

teach_combos

counslor_rat_abbr

alum_combos

Combinations of athletic, personal, extracurricular, and
academic ratings. Each profile rating has categories: 1,
2, 3, 4, 5, or 6. Exact combinations are determined at
the applicant level (e.g. any applicant who received four
ratings of 3 would have the exact combination 3333).
Combinations that appear in the sample at least 100
times have their own control group. The remainder of
combinations are combined with the control group with
the closest admission rate.
Combinations of school support ratings, assigned by
Admissions Committee, based on two teacher
recommendations. Each teacher rating has categories:
1, 2, 3, 4, 5, and Missing. Combinations are determined
at the applicant level (e.g. any applicant who received
ratings of 1 and 2 would have the combination 12).
Combinations that appear in the sample at least 100
times have their own control group. The remainder of
combinations are combined with the control group with
the closest admission rate.
School support rating, assigned by Admissions
Committee, based on applicant’s recommendation from
guidance counselor. Categories: 1, 2, 3, 4, 5, and
Missing.
Combinations of alumni interview overall and personal
ratings. Each alumni interview rating has categories: 1,
2, 3, 4, 5 or 6, and Missing. Combinations are
determined at the applicant level (e.g. any applicant
who received an overall rating of 1 and a personal rating
of 2 would have the combination 12). Combinations that
appear in the sample at least 100 times have their own
control group. The remainder of combinations are
combined with the control group with the closest
admission rate.







HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 182

Card Model
Arcidiacono
Variable

Variable Description

father_occ_cat

Mother’s occupation category; see Appendix C.





mother_occ_cat

Father’s occupation category; see Appendix C.





father_deceased_yn

Indicator for whether father is marked as deceased;
defaulted to false for missing entries





mother_deceased_yn

Indicator for whether mother is marked as deceased;
defaulted to false for missing entries





parent_ivy

Indicator for whether at least one parent attended an
Ivy League school (not counting Ivy sister schools);
defaulted to false for missing entries





























rural

intendedCareer
school_type
legacy_grad
perm_res

staffOP
total_work

primcoll*

staff_yn
born_USA
outside_US_yn

Indicator for whether applicant’s high school county is
not in a Metropolitan or Micropolitan Statistical Area;
for applicants missing high school city field, permanent
address city is used.
Intended career indicated by applicant, from a choice of
15 career categories, "Other," "Undecided," or
"Unknown"
School type (public, private, Catholic, or missing)
Indicator for whether at least one of applicant’s parents
went to Harvard Graduate School
Indicator for whether applicant is a United States
permanent resident
Combination of staff interview overall and personal
ratings. Control groups defined: (1) ratings
combinations of 1 and 2, (2) combinations of 2 and 3,
(3) both ratings of 3, and (4) remainder.
Total hours of work reported in activity description
Indicators for applicant’s primary extracurricular
activities (collapsed into the following groups: (1)
Varsity, JV, or Club athletics; (2) Computer, Speech and
Debate, Journalism, Science, Math, Robotics, or
Academic; (3) Volunteer or Religious; (4)
Environmental, Family, LGBT, School spirit, or Other;
(5) Dance, Drama, or Vocal music; (6) Instrumental
music; (7) Politics; (8) Work; (9) Career; (10) Cultural,
Foreign exchange, or Foreign language; (11) Missing;
and (12) Junior ROTC). A primary activity is defined as
an activity the applicant lists in the first or second
activity field of her application.
Indicator for whether applicant received a staff
interview rating
Indicator for whether applicant was born outside of
United States
Indicator for whether applicant lived outside of United
States

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Pooled

Yearby-year

Variable Name
Contextual Factors









Page 183

Card Model
Variable Name

Variable Description

Arcidiacono
Variable

Pooled

Yearby-year

High School Characteristics
The College Board aggregates applicant-level data to the high school level, based
on student’s AICODE. All high school variables are interacted with the SAT state
indicator unless denoted with †.
Indicator for whether applicant’s state has more SAT
takers than ACT takers that applied to Harvard
sat_state
(a student is marked as an SAT/ACT taker if the
corresponding composite score is available for that
student)
Average score on the math section of the SAT I for all
hs_sat_math
students at applicant’s high school
Average score on the verbal section of the SAT for all
hs_sat_cr
students at applicant’s high school













hs_sat_w

Average score on the writing section of the SAT for all
students at applicant’s high school





hs_english

Percent of students at applicant’s high school who
report that they speak only English





hs_app_outofstate

Percent of students at applicant’s high school who
applied to an out of state college

























hs_avg_num_ap
hs_fin_aid
hs_avg_hon
hs_parent_ed
hs_avg_sat_sends
hs_coll_admit_rate
hs_black†
hs_white†
hs_hispanic†
hs_med_income†
hs_pov_line†
hs_house_val†

Average # of AP tests taken by students at applicant’s
high school
Percent of students at applicant’s high school who
require financial aid for college
Average # of honors courses taken by students at
applicant’s high school
Percent of students at applicant’s high school who
reported that no parent had education beyond high
school
Average number of scores sends for students at
applicant’s high school
Average rate of admission for colleges receiving score
sends from students at applicant’s high school
ACS-based percent of students at applicant’s high
school who are Black
ACS-based percent of students at applicant’s high
school who are White
ACS-based percent of students at applicant’s high
school who are Hispanic
ACS-based median family income of students at
applicant’s high school
ACS-based percent of students at applicant’s high
school who are below the poverty line
ACS-based median value of home for students at
applicant’s high school, as a percentage of average state
value

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY





























Page 184


Card Model
Variable Name
Variable Description
Neighborhood Characteristics
The College Board aggregates applicant-level data to the educational neighborhood
(one or more contiguous census tracts). All neighborhood variables are interacted
with the SAT state indicator unless denoted with †.
Average score on the math section of the SAT for all
n_sat_math
students in applicant’s neighborhood

Arcidiacono
Variable

Pooled

Yearby-year





n_sat_cr

Average score on the verbal section of the SAT for all
students in applicant’s neighborhood





n_sat_w

Average score on the writing section of the SAT for all
students in applicant’s neighborhood





n_english

Percent of students in applicant’s neighborhood who report
that they only speak English

















n_app_outofstate
n_avg_num_ap
n_fin_aid

Percent of students in applicant’s neighborhood who
applied to an out of state college
Average # of AP tests taken by students in applicant’s
neighborhood
Percent of students in applicant’s neighborhood who
require financial aid for college

n_avg_hon

Average # of honors courses taken by students in
applicant’s neighborhood





n_parent_ed

Percent of students in applicant’s neighborhood who
reported that no parent had education beyond high school





n_avg_sat_sends

Average number of score sends for students in applicant’s
neighborhood





n_coll_admit_rate

Average rate of admissions for colleges receiving score
sends from students in applicant’s neighborhood





n_black†

ACS-based percent of students in applicant’s neighborhood
who are Black





n_white†

ACS-based percent of students in applicant’s neighborhood
who are White





n_hispanic†

ACS-based percent of students in applicant’s neighborhood
who are Hispanic





















n_med_income_imp†
n_pov_line_imp†

n_house_val_imp†

ACS-based median family income of students in applicant’s
neighborhood, missing values filled with mean
ACS-based percent of students in applicant’s neighborhood
who are below the poverty line, missing values filled with
mean
ACS-based median value of home for students in
applicant’s neighborhood, as a percentage of average state
value, missing values filled with mean

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 185

Card Model
Variable Name
m_n_pov_line†
m_n_med_income†
m_n_house_val†

Variable Description
Indicator for missing neighborhood poverty line variable
Indicator for missing neighborhood median income
variable
Indicator for missing neighborhood house value variable

Arcidiacono
Variable

Pooled


Yearby-year










Note: I assign parents to be mothers or fathers using the father/mother_type variables for years before 2017, and the
parent1/2_type variables from 2017 and on due to data availability. I assign parents to be “mother figures” (e.g., “mother”,
“aunt”) or “father figures” (e.g., “father”, “grandfather”) using the variables father/mother_type for years before 2017, and using
parent1/2_type from 2017 and on due to data availability. When the parental type variable is gender neutral (e.g., “guardian”), I
use gender information from the parent1/2_gender variable in my assignment.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 186

13. APPENDIX F
13.1. Mr. Kahlenberg’s Simulations

Mr. Kahlenberg's Simulation 1: Impact on class quality and composition
Simulated Class: Removing
Consideration of Race,
Preferences, Athletes;
Preference Disadvantaged
Students [2]
Model
Baseline:
Status Quo [3]

Outcome Measures

Predicted
Value

% Change

1.
2.
3.
4.
5.

Race
White
Asian-American
Hispanic
African-American
Other

40.4%
23.7%
12.9%
13.6%
9.3%

38.3%
34.0%
11.4%
6.6%
9.7%

-5%
+43%
-12%
-51%
+4%

6.
7.
8.
9.

Academic
Average Composite SAT Score
Average Composite ACT Score
Average Converted GPA
Average Academic Index

2239
33.3
77.0
228.2

2235
33.4
77.3
228.7

-0.2%
+0.3%
+0.5%
+0.3%

10.
11.
12.
13.

Fraction with Profile Rating of 1 or 2
Academic
Extracurricular
Personal
Athletic

76%
61%
73%
27%

78%
63%
69%
15%

+2%
+4%
-5%
-44%

14.
15.
16.
17.

Average Profile Rating (higher is worse)
Academic
Extracurricular
Personal
Athletic

2.22
2.40
2.27
3.04

2.19
2.38
2.31
3.41

-1%
-0.4%
+2%
+12%

293
78
186

89
20
14

-70%
-74%
-93%

50

36

-28%

859

844

-2%

Applicant Characteristics
18. Number of Lineage Students
19. Number of Double Lineage Students
20. Number of Recruited Athletes
21.

Number of Children of Harvard Faculty
or Staff

22.

Number of Students on Dean’s and
Director’s Interest Lists

23. Number of Female

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 187

24.
25.
26.
27.

Socioeconomic Status
Number First Generation College
Number Disadvantaged
Number Fee Waiver
Number Financial Aid

28.
29.
30.
31.
32.
33.
34.
35.

Concentrations
Social Sciences
Humanities
Biological Sciences
Physical Science
Engineering
Computer Science
Mathematics
Unspecified

120
305
303
1141

236
808
623
1354

+97%
+165%
+106%
+19%

25%
14%
21%
6.9%
13%
6.1%
6.5%
6.7%

24%
12%
23%
7.8%
15%
6.5%
6.6%
5.1%

-6%
-10%
+9%
+12%
+10%
+6%
+1%
-23%

Source: Arcidiacono Data
Note:
[1] Sample consists of applicants to the class of 2019 in Prof. Arcidiacono’s expanded sample. Prof. Arcidiacono’s Model 6 is used with
interactions between race and year, disadvantaged and year, and with the exclusion of the overall rating.
[2] Mr. Kahlenberg removes consideration of an applicant’s race and lineage status, whether the applicant applied Early Action, whether
the applicant’s parents are Harvard faculty or staff, whether the applicant appeared on the Dean’s or Director’s interest list, whether the
applicant was identified as disadvantaged, whether the applicant applied for a waiver of the application fee, whether the applicant is a firstgeneration college student, whether the applicant applied for financial aid, and whether the applicant is a recruited athlete. In addition,
recruited athletes are assigned extracurricular and athletic ratings of 2. Mr. Kahlenberg gives a boost to disadvantaged applicants by
adding to their admission index a value equal to half the value of the “athlete” coefficient in the model.
[3] This analysis reports values entirely from the predicted class. In his report, Mr. Kahlenberg reports average academic index,
extracurricular rating, and personal rating values from the actul admitted class, while reporting the racial composition and share of
disadvantaged students from the predicted class, under the “Status Quo” specification.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 188

Mr. Kahlenberg's Simulation 2: Impact on class quality and composition

Simulated Class: Removing
Consideration of Race,
Preferences, Athletes;
Preference Disadvantaged
Students; Double Number of
Disadvantaged Applicants [2]
Model
Baseline:
Status Quo [3]

Outcome Measures

Predicted
Value

% Change

1.
2.
3.
4.
5.

Race
White
Asian-American
Hispanic
African-American
Other

40.4%
23.7%
12.9%
13.6%
9.3%

34.6%
34.5%
13.7%
8.0%
9.1%

-14%
+45%
+6%
-41%
-1%

6.
7.
8.
9.

Academic
Average Composite SAT Score
Average Composite ACT Score
Average Converted GPA
Average Academic Index

2239
33.3
77.0
228.2

2214
33.2
77.2
227.6

-1%
-0.3%
+0.4%
-0.2%

10.
11.
12.
13.

Fraction with Profile Rating of 1 or 2
Academic
Extracurricular
Personal
Athletic

76%
61%
73%
27%

74%
63%
71%
14%

-3%
+3%
-2%
-50%

14.
15.
16.
17.

Average Profile Rating (higher is worse)
Academic
Extracurricular
Personal
Athletic

2.22
2.40
2.27
3.04

2.24
2.40
2.29
3.51

+1%
+0.4%
+1%
+15%

293
78
186

61
14
11

-79%
-82%
-94%

50

28

-44%

859

842

-2%

Applicant Characteristics
18. Number of Lineage Students
19. Number of Double Lineage Students
20. Number of Recruited Athletes
21.

Number of Children of Harvard Faculty
or Staff

22.

Number of Students on Dean’s and
Director’s Interest Lists

23. Number of Female

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 189

24.
25.
26.
27.

Socioeconomic Status
Number First Generation College
Number Disadvantaged
Number Fee Waiver
Number Financial Aid

28.
29.
30.
31.
32.
33.
34.
35.

Concentrations
Social Sciences
Humanities
Biological Sciences
Physical Science
Engineering
Computer Science
Mathematics
Unspecified

120
305
303
1141

328
1197
885
1503

+173%
+292%
+192%
+32%

25%
14%
21%
6.9%
13%
6.1%
6.5%
6.7%

24%
12%
24%
8.1%
15%
6.1%
6.2%
4.9%

-5%
-13%
+13%
+16%
+11%
-0.1%
-5%
-26%

Source: Arcidiacono Data
Note:
[1] Sample consists of applicants to the class of 2019 in Prof. Arcidiacono’s expanded sample. Prof. Arcidiacono’s Model 6 is used with
interactions between race and year, disadvantaged and year, and with the exclusion of the overall rating.
[2] Mr. Kahlenberg doubles the number of disadvantaged applicants. Mr. Kahlenberg removes consideration of an applicant’s race and
lineage status, whether the applicant applied Early Action, whether the applicant’s parents are Harvard faculty or staff, whether the
applicant appeared on the Dean’s or Director’s interest list, whether the applicant was identified as disadvantaged, whether the applicant
applied for a waiver of the application fee, whether the applicant is a first-generation college student, whether the applicant applied for
financial aid, and whether the applicant is a recruited athlete. In addition, recruited athletes are assigned extracurricular and athletic
ratings of 2. Mr. Kahlenberg gives a boost to disadvantaged applicants by adding to their admission index a value equal to half the value of
the “athlete” coefficient in the model.
[3] This analysis reports values entirely from the predicted class. In his report, Mr. Kahlenberg reports average academic index,
extracurricular rating, and personal rating values from the actul admitted class, while reporting the racial composition and share of
disadvantaged students from the predicted class, under the “Status Quo” specification.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 190

Mr. Kahlenberg's Simulation 3: Impact on class quality and composition

Simulated Class Using ClusterBased Admission: Removing
Consideration of Race,
Preferences, Athletes;
Preference Disadvantaged
Students [2]
Model
Baseline:
Status Quo [3]

Outcome Measures

Predicted
Value

% Change

1.
2.
3.
4.
5.

Race
White
Asian-American
Hispanic
African-American
Other

40.4%
23.7%
12.9%
13.6%
9.3%

37.9%
29.5%
13.7%
9.6%
9.3%

-6%
+24%
+6%
-29%
+0.4%

6.
7.
8.
9.

Academic
Average Composite SAT Score
Average Composite ACT Score
Average Converted GPA
Average Academic Index

2239
33.3
77.0
228.2

2206
33.1
77.2
227.0

-1%
-1%
+0.3%
-1%

10.
11.
12.
13.

Fraction with Profile Rating of 1 or 2
Academic
Extracurricular
Personal
Athletic

76%
61%
73%
27%

70%
58%
66%
14%

-8%
-5%
-9%
-49%

14.
15.
16.
17.

Average Profile Rating (higher is worse)
Academic
Extracurricular
Personal
Athletic

2.22
2.40
2.27
3.04

2.28
2.45
2.34
3.50

+2%
+2%
+3%
+15%

293
78
186

66
16
9

-77%
-80%
-95%

50

28

-43%

859

844

-2%

Applicant Characteristics
18. Number of Lineage Students
19. Number of Double Lineage Students
20. Number of Recruited Athletes
21.

Number of Children of Harvard Faculty
or Staff

22.

Number of Students on Dean’s and
Director’s Interest Lists

23. Number of Female

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 191

24.
25.
26.
27.

Socioeconomic Status
Number First Generation College
Number Disadvantaged
Number Fee Waiver
Number Financial Aid

28.
29.
30.
31.
32.
33.
34.
35.

Concentrations
Social Sciences
Humanities
Biological Sciences
Physical Science
Engineering
Computer Science
Mathematics
Unspecified

120
305
303
1141

294
996
784
1465

+145%
+227%
+159%
+28%

25%
14%
21%
6.9%
13%
6.1%
6.5%
6.7%

23%
12%
23%
8.2%
15%
6.5%
7.4%
5.4%

-9%
-16%
+6%
+18%
+14%
+6%
+13%
-19%

Source: Arcidiacono Data
Note:
[1] Sample consists of applicants to the class of 2019 in Prof. Arcidiacono’s expanded sample. Prof. Arcidiacono’s Model 6 is used with
interactions between race and year, disadvantaged and year, and with the exclusion of the overall rating.
[2] Applicants are ranked in descending order of admission index and an equal number of applicants are admitted from each
neighborhood cluster. Mr. Kahlenberg removes consideration of an applicant’s race and lineage status, whether the applicant applied Early
Action, whether the applicant’s parents are Harvard faculty or staff, whether the applicant appeared on the Dean’s or Director’s interest
list, whether the applicant was identified as disadvantaged, whether the applicant applied for a waiver of the application fee, whether the
applicant is a first-generation college student, whether the applicant applied for financial aid, and whether the applicant is a recruited
athlete. In addition, recruited athletes are assigned extracurricular and athletic ratings of 2. Mr. Kahlenberg gives a boost to disadvantaged
applicants by adding to their admission index a value equal to half the value of the “athlete” coefficient in the model.
[3] This analysis reports values entirely from the predicted class. In his report, Mr. Kahlenberg reports average academic index,
extracurricular rating, and personal rating values from the actul admitted class, while reporting the racial composition and share of
disadvantaged students from the predicted class, under the “Status Quo” specification.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 192

Mr. Kahlenberg's Simulation 4: Impact on class quality and composition

Simulated Class Using ClusterBased Admission: Removing
Consideration of Race,
Preferences; Preference
Disadvantaged Students [2]
Model
Baseline:
Status Quo [3]

Outcome Measures

Predicted
Value

% Change

1.
2.
3.
4.
5.

Race
White
Asian-American
Hispanic
African-American
Other

40.4%
23.7%
12.9%
13.6%
9.3%

39.6%
27.6%
13.5%
10.1%
9.2%

-2%
+16%
+4%
-26%
-1%

6.
7.
8.
9.

Academic
Average Composite SAT Score
Average Composite ACT Score
Average Converted GPA
Average Academic Index

2239
33.3
77.0
228.2

2191
32.9
77.1
225.9

-2%
-1%
+0.2%
-1%

10.
11.
12.
13.

Fraction with Profile Rating of 1 or 2
Academic
Extracurricular
Personal
Athletic

76%
61%
73%
27%

66%
54%
65%
20%

-13%
-13%
-10%
-25%

14.
15.
16.
17.

Average Profile Rating (higher is worse)
Academic
Extracurricular
Personal
Athletic

2.22
2.40
2.27
3.04

2.33
2.50
2.34
3.31

+5%
+4%
+3%
+9%

293
78
186

57
12
150

-81%
-85%
-20%

50

19

-62%

859

827

-4%

Applicant Characteristics
18. Number of Lineage Students
19. Number of Double Lineage Students
20. Number of Recruited Athletes
21.

Number of Children of Harvard Faculty
or Staff

22.

Number of Students on Dean’s and
Director’s Interest Lists

23. Number of Female

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 193

24.
25.
26.
27.

Socioeconomic Status
Number First Generation College
Number Disadvantaged
Number Fee Waiver
Number Financial Aid

28.
29.
30.
31.
32.
33.
34.
35.

Concentrations
Social Sciences
Humanities
Biological Sciences
Physical Science
Engineering
Computer Science
Mathematics
Unspecified

120
305
303
1141

289
949
753
1450

+141%
+211%
+149%
+27%

25%
14%
21%
6.9%
13%
6.1%
6.5%
6.7%

23%
11%
23%
7.7%
15%
6.1%
7.5%
6.2%

-7%
-19%
+8%
+11%
+11%
-0.2%
+15%
-7%

Source: Arcidiacono Data
Note:
[1] Sample consists of applicants to the class of 2019 in Prof. Arcidiacono’s expanded sample. Prof. Arcidiacono’s Model 6 is used with
interactions between race and year, disadvantaged and year, and with the exclusion of the overall rating.
[2] Applicants are ranked in descending order of admission index and an equal number of applicants are admitted from each
neighborhood cluster. Mr. Kahlenberg removes consideration of an applicant’s race and lineage status, whether the applicant applied Early
Action, whether the applicant’s parents are Harvard faculty or staff, whether the applicant appeared on the Dean’s or Director’s interest
list, whether the applicant was identified as disadvantaged, whether the applicant applied for a waiver of the application fee, whether the
applicant is a first-generation college student, and whether the applicant applied for financial aid. Mr. Kahlenberg gives a boost to
disadvantaged applicants by adding to their admission index a value equal to half the value of the “athlete” coefficient in the model.
[3] This analysis reports values entirely from the predicted class. In his report, Mr. Kahlenberg reports average academic index,
extracurricular rating, and personal rating values from the actul admitted class, while reporting the racial composition and share of
disadvantaged students from the predicted class, under the “Status Quo” specification.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 194

Mr. Kahlenberg's Simulation 5: Impact on class quality and composition
Simulated Class Using ClusterBased Admission: Removing
Consideration of Race,
Preferences, Athletes;
Preference Disadvantaged
Students; Double Number of
Disadvantaged Applicants [2]
Model
Baseline:
Status Quo [3]

Outcome Measures

Predicted
Value

% Change

1.
2.
3.
4.
5.

Race
White
Asian-American
Hispanic
African-American
Other

40.4%
23.7%
12.9%
13.6%
9.3%

36.1%
30.0%
14.6%
10.6%
8.8%

-11%
+26%
+13%
-22%
-5%

6.
7.
8.
9.

Academic
Average Composite SAT Score
Average Composite ACT Score
Average Converted GPA
Average Academic Index

2239
33.3
77.0
228.2

2205
33.1
77.1
226.8

-2%
-1%
+0.2%
-1%

10.
11.
12.
13.

Fraction with Profile Rating of 1 or 2
Academic
Extracurricular
Personal
Athletic

76%
61%
73%
27%

70%
60%
70%
13%

-8%
-2%
-3%
-52%

14.
15.
16.
17.

Average Profile Rating (higher is worse)
Academic
Extracurricular
Personal
Athletic

2.22
2.40
2.27
3.04

2.28
2.44
2.30
3.54

+2%
+2%
+1%
+16%

293
78
186

48
13
11

-84%
-83%
-94%

50

23

-54%

859

814

-5%

Applicant Characteristics
18. Number of Lineage Students
19. Number of Double Lineage Students
20. Number of Recruited Athletes
21.

Number of Children of Harvard Faculty
or Staff

22.

Number of Students on Dean’s and
Director’s Interest Lists

23. Number of Female

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 195

24.
25.
26.
27.

Socioeconomic Status
Number First Generation College
Number Disadvantaged
Number Fee Waiver
Number Financial Aid

28.
29.
30.
31.
32.
33.
34.
35.

Concentrations
Social Sciences
Humanities
Biological Sciences
Physical Science
Engineering
Computer Science
Mathematics
Unspecified

120
305
303
1141

349
1298
971
1561

+191%
+326%
+221%
+37%

25%
14%
21%
6.9%
13%
6.1%
6.5%
6.7%

24%
12%
23%
8.4%
15%
5.3%
6.7%
4.9%

-3%
-15%
+9%
+21%
+15%
-12%
+4%
-27%

Source: Arcidiacono Data
Note:
[1] Sample consists of applicants to the class of 2019 in Prof. Arcidiacono’s expanded sample. Prof. Arcidiacono’s Model 6 is used with
interactions between race and year, disadvantaged and year, and with the exclusion of the overall rating.
[2] Mr. Kahlenberg doubles the number of disadvantaged applicants. Applicants are ranked in descending order of admission index and an
equal number of applicants are admitted from each neighborhood cluster. Mr. Kahlenberg removes consideration of an applicant’s race
and lineage status, whether the applicant applied Early Action, whether the applicant’s parents are Harvard faculty or staff, whether the
applicant appeared on the Dean’s or Director’s interest list, whether the applicant was identified as disadvantaged, whether the applicant
applied for a waiver of the application fee, whether the applicant is a first-generation college student, whether the applicant applied for
financial aid, and whether the applicant is a recruited athlete. In addition, recruited athletes are assigned extracurricular and athletic
ratings of 2. Mr. Kahlenberg gives a boost to disadvantaged applicants by adding to their admission index a value equal to half the value of
the “athlete” coefficient in the model.
[3] This analysis reports values entirely from the predicted class. In his report, Mr. Kahlenberg reports average academic index,
extracurricular rating, and personal rating values from the actul admitted class, while reporting the racial composition and share of
disadvantaged students from the predicted class, under the “Status Quo” specification.

HIGHLY CONFIDENTIAL – ATTORNEYS’ EYES ONLY

Page 196