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).

EXHIBIT 112

From:

Bever, Erica Jane [[e-mail] ]

Sent:

Wednesday, May 01, 2013 5:56:26 PM

To:

Fitzsimmons, William

CC:

Pacholok, Olesia; Driver-Linn, Erin; Hansen, Mark Francis

Subject:

Admissions memo

Attachments:

LowIncomeAdmissionMemo_FINAL_20130501.pdf

Dear Fitz,
Attached is a memo describing our recent analysis of low income admissions. In the memo we describe our approach
and results. At your suggestion, we reviewed a small sample of literature to put this in context and realized our approach
was consistent with what others have done. We'd appreciate any comments or suggestions you have.
We thought, based on our conversation last week, that it would also make sense to share this with Jeff Neal and
Christine Heenan, Nina Collins, and Sally Donahue. Does that make sense? Are there others you would like to include in
this conversation?
Let us know if you have any questions!
Best,
Erica
Erica Bever
Assistant Director, Office of Institutional Research
Harvard University
Holyoke Center Suite 780
1350 Massachusetts Avenue
Cambridge, MA 02138
617-495-2718

CONFIDENTIAL

HARV00023547

CONFIDENTIAL DRAFT

To:
Bill Fitzsimmons
From: Erica Bever, Erin Driver-Linn, Mark Hansen
Re:
Date:

Harvard College Admissions and Low Income Students
May 1, 2013

As you have discussed with us, there may be value in responding to recent press about the rate of
admission for low income students at elite institutions and in particular for Harvard College. Critics like
Bill Bowen have suggested for years that need-blind admissions policies prohibit Harvard and others
from using important information to evaluate the application of a low income student. In Equity and
Excellence in American Higher Education, Bowen, Kurzweil, and Tobin note that,"We see that there was
no perceptible difference in the chances of being admitted, at any given SAT level, for students from the
two low-SES categories and for all other(non-minority) students"(Bowen, Kurzweil, & Tobin, 2005).
However, the reality in admissions may be more complex than need-blind policies suggest as noted in
Caroline Hoxby and Chris Avery's recent study:"many admissions officers say that they use students'
essays, teachers' letters, parents' education, attendance at an a 'under-resourced' high school, and
similar indicators to identify, provide favorable terms of admission to, and strongly recruit students who
they believe to be economically disadvantaged"(Hoxby & Avery, 2012). At your request, we undertook
an analysis to determine if the chance of admission is any different for low income students, holding all
other admissions characteristics constant.
Below, we briefly describe the data used for our analysis and its limitations, our approach, and our
findings. At the conclusion, we outline some issues we believe are important to consider prior to public
dissemination of this analysis.
Data Sources and Limitations
Applicant data was provided to the Office of Institutional Research by the Office of Admission. Data on
income comes from the CSS profile section of the financial aid application and was supplied to the Office
of Institutional Research by the Financial Aid Office for the classes of 2009 to 2016. Of the 192,359
students who applied for admission for those classes,49% also submitted the CSS profile portion of the
financial aid application. We do not have income data for students who did not apply for aid.
Analysis Approach and Results
Similar to the analyses conducted by Bowen et. al. in Equity and Excellence in American Higher
Education, we first examine the admit rate of low-income applicants (defined as applicants with family
incomes less than or equal to $60,000) by a measure of academic qualification (such as SAT score) to see
if there is any evidence of a preference for low-income applicants. If groups of applicants with similar
academic qualifications, but different incomes, are admitted at different rates, this might suggest the
presence of a "tip" for low-income applicants.
Exhibit 1 illustrates the relationship between income and SAT I score. Fewer than 20% of applicants in
the lowest income group (Less than $10K) have SAT I scores above 750, while almost 30% have scores

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below 600, where the admission rates are below 1%, without controlling for additional factors. As
incomes increase, the proportion of students with SAT I scores above 750 increases, while the
proportion with scores below 600 decreases. Based on a preference for high SAT scores in the
admission process (applicants with SAT I scores lower than 600 have a very low chance of admission),
we would expect that applicants from low-income families would be admitted at a lower rate. However,
for all SAT I scores greater than 600, we see that applicants from families with incomes less than or
equal to $60,000 are admitted at a higher rate than applicants with similar SAT scores from families with
higher incomes (Exhibit 2).
The differences noted above could be related to other factors important in the admissions process. In
order to control for those potential issues, we implement a logistic regression model to predict the
probability of admission, controlling for demographic characteristics and a variety of metrics used to
asses qualification for admission. Demographic characteristics include gender and race/ethnicity.
Qualifications used in admission include academic index, academic rating, extracurricular rating,
personal rating, athletic rating, and legacy status.
This approach has several limitations; we picked a small set of variables that would factor in admissions
decisions. The selection of a wider set of variables might result in a better fitting model, one that
accounts for more of the variation in individual applicants and their potentially unique contributions to
the entering class. For example, the model does not capture exceptional talent in art or music explicitly
(
although ratings may capture some aspect of these attributes). In addition, our model is limited to
main effects, not examining interactions between variables. Our analysis should not be considered
exhaustive.
In spite of these limitations, the logistic regression model results are consistent with the descriptive
analysis described above and shown in Exhibits 1 and 2. Exhibit 3 illustrates the difference between the
predicted admission rate and actual admission rate for students at each income level. The predicted
rate controls for demographics, legacy status, athletic skills, ratings, and measures of academic
qualifications. Given what we know about the relationship between income and SAT scores and the
extracurricular opportunities available to low income applicants, we would expect low income
applicants to be admitted at lower rates than their peers (this is reflected in predicted admit rates).
However, we find actual admission rates indicate that applicants with incomes below $120K are
admitted at higher rates than we expected.
To get a sense of the size of the admissions advantage conferred to low-income applicants relative to
other groups of applicants, the so-called "thumb on the scale," we include low-income status in a
second logistic regression model. The table below is sorted based on the effect size of each of the
variables included in the model. The variables with the largest effects on the probability of admission
are athletic rating, personal rating, and legacy status. Compared to athletes and legacies, the size of the
advantage for low income students is relatively small.
Table: Logistic Regression Predicting Admission from Classes 2009 through 2016
Variable

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Coefficient

P-value

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Estimate
Athletic rating of 1
Personal Rating 1 or 2
Legacy
African American
Native American
Extracurricular 1 or 2
Academic 1 or 2
Standardized Academic Index
Hispanic
CSS self-reported income less than or equal to $60K
International
Asian
Constant
Unknown/Other
Female

6.33
2.41
2.40
2.37
1.73
1.58
1.31
1.29
1.27
0.98
0.24
-0.37
-6.23
-0.03
0.00

0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.41
0.87

N = 192,359; Pseduo R2 = 0.45

The relative sizes of the admissions advantage conferred on different groups can be seen by looking at
the differences in actual admit rates as well. In Exhibit 4, we limit our analysis to students with high
academic ratings(1 or 2) and examine the differences between athletes and non-athletes, legacy
students and others, Asian students and all other students, and low income students and all other
students. An athlete that is also an academic 1 or 2 has an admit rate of 83% compared against 16% for
non-athletes with an academic 1 or 2. Fifty-five percent of legacies who are academic is and 2s are
admitted compared with 15% of all other academic 1 and 2s. Asian applicants with an academic 1 or 2
are admitted 12% of the time compared against an admit rate of 18% for non-Asian applicants. By
comparison, low income applicants with an academic 1 or 2 have an admit rate of 24% compared
against 15% for all other applicants.
Issues to consider before sharing these results publicly
We imagine that sharing any analysis of admission weights will draw attention to the variety of factors
that compete with one another in the admissions decision. To state the obvious, with only -2,200
spaces for admitted students per year, implicit tradeoffs are made between athletes and non-athletes,
legacy admits and those without affiliation, low income and other students. We know that many are
interested in the analysis of the relative tradeoffs. While we find that low income students clearly
receive a "tip" in the admissions process, our descriptive analysis and regression models also shows that
the tip for legacies and athletes is larger and that there are demographic groups that have negative
effects.

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Works Cited
Bowen, W. G., Kurzweil, M. A., & Tobin, E. M.(2005). Equity and Excellence in American Higher
Education. Charlottesville and London: University of Virginia Press.
Hoxby, C. M., & Avery, C.(2012, December). The Missing "One-Offs": The Hidden Supply of HighAchieving, Low Income Students. NBER Working Paper Series.

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Exhibit 1: Distribution of Applicant Average SAT I Scores by Income, Classes of 2009-2016
CONFIDENTIAL DRAFT

• Income and SAT scores are positively related.

100% 90% 80% 70% 60% 50% 40% -

750+
•

3
0% -

600-750
•

20% -

•
Less than 600

10% I
I
t
I
l
e
0 0 0 0 0 0 0 0
a—I CV
Cfl Sr
Lfl
o r•-• co

-to. 0000000
m

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I

I

0

I

0 0 0 0 0 0 0 0 0 0 0 0
rO St in to r-• co cri 0 a—I CV
CV CV
CV

0
030 0 0 0 0 0 0 0 0 0
VT CI 0 a—I (NCO Sr in t..0 h CO
a—I a—I
t
r} 4.4
tr}

+
0 0 0
m
in 0
CV CV (-4 In
esi

0 0 0
Oi 0
t Nr (NJ
•
in. in. tn.

0 0 0
CV CO St
rsi

No Aid Application

0
%

HARV00023552

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Exhibit 2: Admit Rates by Income and SAT Score, Class of 2009-2016

CONFIDENTIAL DRAFT

• Using SAT as a proxy for admissions qualifications, we see at every score level, lower income students have higher
admit rates.

40% -

Less than or
equal to $60K

35%

30%

Greater than
$60K*

25%

20%

15%

10%

5%

0
%
60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

79

80

Notes:
The analysis above uses the average of the maximum math, writing, and reading scores a student received. Average SAT I Scores less than 600 are excluded from the
exhibit above asgtinpateiroglfiiitepkatatpglefeksomifh SAT I scores less than 600 have an admit rate of less than 1%.
HARV00023553
* Category includes those with no (missing) self-reported income

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Exhibit 3: Predicted and Actual Admit Rates by Income, Classes of 2009-2016

3
CONFIDENTIAL DRAFT

Predicted and Actual Admit Rates by Income Band
14%

12%

11%
11%

10%
10%
9%

10%

8%
13%
6%
11%
10%
9%

4%

8%

8%
6
%

2%

0%
$0-40K

$40-80K

•
Predicted Admit Rate

$80K-120K

$120K-160K

$160K-$200K

0 Difference Between Predicted and Actual Admit Rates

$200K+

unknown

•
Actual Admit Rate

• Predicted admit rates by income are based on logistic regression models that control for academic
index, academic rating, athlete, legacy, extracurricular rating, personal rating, ethnicity, and gender.
• Low income students are admitted at higher rates than predicted. Higher income students are
admitted at a lower rate.
• Admit model has a pseudo R-squared of 0.44

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HARV00023554

Exhibit 4: Admit Rates by Selected Characteristics, Classes of 2009-2016

CONFIDENTIAL DRAFT

• Among top academic achievers (academic rating = 1 or 2), those who are athletes or legacies have much higher
rates of admission.
• Low income achievers also have higher rates of admission.
• Asian high achievers have lower rates of admission.
Average Admit Ratesfor Top Academic Achievers(Academic Rating 1 or 2)by Selected Demographic Characteristics
90% —

80% — - - -

70% — - - -

60% — - - - -

50% — - - -

40% — - - -

3
0% — - - Admit Rate for All
Academic 1 & 2 =
16%

20% — - - - -

0
%
Athlete
# in group
# not in group

Legacy

Low Income

Asian

501

2,636

7,065

24,692

74,755

72,620

68,191

50,564

•
Admit rate for group
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Amit Rate for those not in group
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