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

Filing 546

MOTION in Limine by Students for Fair Admissions, Inc.. (Attachments: # 1 Exhibit 1, # 2 Exhibit 2, # 3 Exhibit 3, # 4 Exhibit 4, # 5 Exhibit 5)(Hughes, John)

Exhibit 2

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 1 of 123

Plaintiff

Defendant

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 2 of 123

1.1. Assignment ....................................................................................................................................3
1.2. Summary of opinions ....................................................................................................................3

2.1. Harvard’s admissions process seeks to find candidates with “distinguishing
excellences” across a variety of dimensions, not just academic achievement ......................10
2.2. Harvard’s admissions process collects a lot of information on non-academic
performance ............................................................................................................................12
2.3. Asian-American and White applicants possess different qualifications and
backgrounds, on average, across a variety of dimensions .....................................................15

3.1. The personal rating is an important factor in admissions decisions, and excluding it
from the admissions model is not justified .............................................................................19
3.2. There is no basis for Prof. Arcidiacono’s decision to exclude parental occupation,
intended career, or staff interviews ........................................................................................34
3.3. Prof. Arcidiacono’s use of a pooled model is inconsistent with an essential feature of
Harvard’s admissions process and thus has no methodological basis ..................................43
3.4. Prof. Arcidiacono’s decision to exclude certain types of applicants from his model is
inconsistent with how Harvard’s admissions process works, and is methodologically
unsound ...................................................................................................................................46

4.1. My preferred regression model shows no evidence of bias against Asian-American
applicants ................................................................................................................................53
4.2. Analysis of key subgroups of the data provides further evidence that there is no bias in
Harvard’s admissions process ................................................................................................60
4.3. Prof. Arcidiacono’s new allegation of bias against dockets with larger shares of
Asian-American applicants lacks any causal credibility .......................................................64
4.4. Other technical criticisms of my model do not change my findings..........................................67

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 3 of 123

5.1. Race alone is uninformative in Harvard’s decision process .....................................................69
5.2. The fact that race has a relatively large effect on the probability of admissions for
some candidates cannot be taken as evidence that race is “determinative”..........................71

6.1. The record does not support Prof. Arcidiacono’s claim of a floor on single-race
African-American admissions starting with the class of 2017 ..............................................76
6.2. The pattern that Prof. Arcidiacono claims as evidence of manipulation is not as
unlikely as he suggests ............................................................................................................79
6.3. The relative quality of single-race African-American admitted students did not fall
starting with the class of 2017, further undermining the idea of a floor on their
admission rate .........................................................................................................................80

7.1. The academic literature establishes that race-neutral alternatives diminish selective
universities’ ability to select on quality ..................................................................................86
7.2. Mr. Kahlenberg’s new simulations confirm that the substitution of race-neutral
alternatives for Harvard’s race-conscious admissions process would change the
characteristics of the class and compromise its quality .........................................................90
7.3. Other race-neutral alternatives are unlikely to generate diversity without changing
class characteristics and compromising class quality ............................................................97
7.4. Conclusion ................................................................................................................................101

8.1. Documents Relied Upon ...........................................................................................................103

9.1. Appendix B.1 Constructing categories for parental occupations ............................................108
9.2. Appendix B.2: Error in Prof. Arcidiacono’s difference-in-difference estimates ....................112
9.3. Appendix B.3: Using absolute deviation to measure the importance of unobserved
characteristics is appropriate ................................................................................................113

10.1. List of variables included in model of admission ...................................................................116

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 4 of 123

1.1. Assignment

1.2. Summary of opinions

the underlying process Harvard employs

that Harvard considers in the

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 5 of 123

admissions process.

should

Students for Fair Admissions, Inc. v. President and Fellows of Harvard College (Harvard
Corporation)

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 6 of 123

Students for Fair Admissions, Inc. v. President and Fellows of Harvard
College (Harvard Corporation)

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 7 of 123

against

in favor

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 8 of 123

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 9 of 123

positive

any

multiple

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 10 of 123

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 11 of 123

2.1. Harvard’s admissions process seeks to find candidates with “distinguishing excellences” across
a variety of dimensions, not just academic achievement

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 12 of 123

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 13 of 123

Academic excellence is abundant in Harvard’s applicant pool

Source: Arcidiacono Data
Note: Data are from applicants to the class of 2019 in Prof. Arcidiacono’s original expanded sample including athletes. Harvard converts
applicant GPAs to a 35-80 scale.

2.2. Harvard’s admissions process collects a lot of information on non-academic performance

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 14 of 123

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 15 of 123

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 16 of 123

only

2.3. Asian-American and White applicants possess different qualifications and backgrounds, on
average, across a variety of dimensions

Students for Fair Admissions, Inc. v. President and Fellows of Harvard College
(Harvard Corporation)

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 17 of 123

White and Asian-American applicants excel in different dimensions

Source: Arcidiacono Data
Note: Data are from applicants to the classes of 2014 – 2019 in Prof. Arcidiacono’s original expanded sample including athletes. Ratings of
2- and above are considered “2 or Better” in this analysis. +/- rating designations were introduced beginning with the class of 2019.

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 Prof. Arcidiacono’s original expanded sample including athletes.

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 18 of 123

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 19 of 123

Introduction to Econometrics

Southern Economic Journal

Introduction to Econometrics

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 20 of 123

3.1. The personal rating is an important factor in admissions decisions, and excluding it from the
admissions model is not justified

3.1.1. Prof. Arcidiacono’s personal ratings regression is missing critical information

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 21 of 123

double

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 22 of 123

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 23 of 123

relatively

favor
personal

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 24 of 123

3.1.2. The data show that, on average, Asian-American applicants are weaker on non-academic
factors that affect the personal rating

unobservable

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 25 of 123

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 26 of 123

less likely

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 27 of 123

Among applicants with an academic rating of 2, White applicants tend to have stronger school
support and alumni ratings than Asian-American applicants

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

stronger

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 28 of 123

For a given academic rating, White applicants tend to have stronger school support and alumni
ratings than Asian-American applicants

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

observable
unobservable

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 29 of 123

Among applicants with an academic rating of 2, White applicants have stronger non-academic
ratings (school support, alumni, and profile other than academic)

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

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 30 of 123

For a given academic rating, White applicants have stronger non-academic ratings (school
support, alumni, and profile other than academic)

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

academic

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 31 of 123

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 32 of 123

White applicants rank higher than Asian-American applicants on non-academic admissions index
Non-Academic
Admissions
Index Decile

White

AsianAmerican

AfricanAmerican

Hispanic

Without Removing Additional Effects
1. 5 or lower

46.46%

51.80%

55.27%

54.33%

2. 6

10.10%

10.35%

9.04%

9.48%

3. 7

10.12%

10.53%

9.00%

9.26%

4. 8

10.47%

10.10%

8.85%

8.90%

5. 9

10.81%

9.47%

9.06%

9.18%

6. 10

12.03%

7.75%

8.79%

8.85%

Remove Effect of ALDC “Tips”
7. 5 or lower

46.98%

51.28%

55.04%

53.81%

8. 6

10.20%

10.24%

8.95%

9.42%

9. 7

10.31%

10.25%

8.82%

9.39%

10. 8

10.69%

9.98%

8.80%

8.58%

11. 9

10.80%

9.56%

9.02%

9.02%

12. 10

11.03%

8.69%

9.37%

9.77%

Remove Effect of Personal Rating
13. 5 or lower

46.45%

51.59%

55.70%

54.63%

14. 6

10.36%

10.14%

9.02%

9.16%

15. 7

10.30%

10.19%

8.90%

9.27%

16. 8

10.34%

10.28%

9.13%

8.96%

17. 9

10.61%

9.84%

8.76%

9.02%

18. 10

11.94%

7.96%

8.50%

8.96%

Remove Effect of Personal Rating and ALDC “Tips”
19. 5 or lower

47.02%

50.92%

55.42%

54.20%

20. 6

10.46%

10.02%

8.89%

9.14%

21. 7

10.59%

9.91%

8.79%

9.06%

22. 8

10.53%

10.02%

9.12%

8.77%

23. 9

10.84%

9.69%

8.64%

8.96%

24. 10

10.56%

9.42%

9.14%

9.88%

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 expanded sample including athletes. The non-academic
admissions index is constructed using the updated approach put forth by Prof. Arcidiacono in Tables 7.4R and 7.5R in Appendix C of his
rebuttal report. The shares within each panel for a given race sum to 100%.

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 33 of 123

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 34 of 123

applicants to
Harvard

only one

American Economic Review: Papers & Proceedings
Economics of
Education Review
Industrial and Labor
Relations Review

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 35 of 123

There is no consistent or statistically significant evidence of bias against Asian-American
applicants even adjusting for what Prof. Arcidiacono alleges as bias in the 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 from
updated Card model using adjusted academic, extracurricular, and personal ratings. Data are from applicants to the classes of 2014 – 2019
in Prof. Arcidiacono’s expanded sample including athletes. An applicant’s adjusted rating is the rating with the highest predicted
probability according to Prof. Arcidiacono’s rating model excluding other profile ratings from the controls and turning off the effect of
race. * indicates significance at the 5% level. Marginal effects are reported as percentage point values.

3.2. There is no basis for Prof. Arcidiacono’s decision to exclude parental occupation, intended
career, or staff interviews

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 36 of 123

3.2.1. Parental Occupation

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 37 of 123

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 38 of 123

Review
and Social Status

–

The American Economic
Occupations
Socioeconomic Status,

Parenting, and Child Development

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 39 of 123

Review
and Social Status

The American Economic
Occupations
Socioeconomic Status,

Parenting, and Child Development

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 40 of 123

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 41 of 123

within that same year

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 42 of 123

3.2.2. Intended Career

Sociology of Education

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 43 of 123

3.2.3. Staff Interviews

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 44 of 123

3.3. Prof. Arcidiacono’s use of a pooled model is inconsistent with an essential feature of Harvard’s
admissions process and thus has no methodological basis

within a year
years

in different

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 45 of 123

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 46 of 123

Estimating a model either pooled or year-by-year will produce extremely similar measures of
statistical precision

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Models are estimated on Prof. Arcidiacono’s expanded sample including athletes. The first panel shows standard errors for Prof.
Arcidiacono’s model estimated year-by-year. The overall standard error (0.15) is the standard error for the weighted average of the yearly
effects. The second panel shows the standard error for Prof. Arcidiacono’s pooled model. The third panel shows the overall standard error
for the weighted average of the yearly effects as estimated from the Card model.

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 47 of 123

3.4. Prof. Arcidiacono’s decision to exclude certain types of applicants from his model is inconsistent
with how Harvard’s admissions process works, and is methodologically unsound

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 48 of 123

3.4.1. Prof. Arcidiacono’s claim that ALDC candidates are part of a “special” admissions process,
and, thus, do not compete with other candidates is not supported by the data, documents, or
depositions

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 49 of 123

does not

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 50 of 123

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 51 of 123

ALDC applicants have higher predicted probabilities of admission than non-ALDC applicants, even
without their ALDC “tip”

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 expanded sample including athletes. ALDC applicants’
predicted probability of admission is calculated removing the effect of being an ALDC applicant (i.e. removing the effect of being a
recruited athlete, on the Dean’s or Director’s list, a lineage applicant, or a child of Harvard faculty and staff).

3.4.2. Prof. Arcidiacono’s claim that ALDC candidates should be excluded because there is no
disparity in admissions decisions for such candidates is methodologically unsound

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 52 of 123

not

is

all

outside of the data

positive

higher

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 53 of 123

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 54 of 123

positive

4.1. My preferred regression model shows no evidence of bias against Asian-American applicants

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 55 of 123

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 Prof.
Arcidiacono’s expanded sample including athletes. * indicates significance at the 5% level. Marginal effects are reported as percentage
point values.

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 56 of 123

Prof. Arcidiacono’s modeling decisions overstate the effect of Asian-American ethnicity on
admissions

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: [1] Data are from Prof. Arcidiacono’s sample. Marginal effects are calculated relative to White applicants. * indicates significance at
the 5% level. Marginal effects are reported as percentage point values. [2] ALDC applicants include lineage applicants, children of Harvard
faculty and staff, recruited athletes, and applicants on the Dean or Director’s interest lists. Such applicants are added to the sample and
indicators for ALDC groups are added to the model. [3] Additional controls include measures of participation in extracurricular activities
and indicators for being born in the United States and having lived outside of the United States. [4] Includes interactions of female with
intended concentration and race, interactions of race with indicator for Early Action, and interactions of race with missing SAT 2 average,
missing alumni rating, and indicator for having a converted GPA of 35.

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 57 of 123

There is no consistent or statistically significant evidence of bias against Asian-American
applicants even when the effect of ALDC status is allowed to vary by race

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Table shows average marginal effect of race on admission for Asian-American applicants relative to White applicants using the
updated Card model with interactions of race and indicators for ALDC groups. * indicates significance at the 5% level. Marginal effects are
reported as percentage point values.

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 58 of 123

There is no consistent or statistically significant evidence of bias against Asian-American
applicants even when the effect of disadvantaged status is allowed to vary by race

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 the
updated Card model with interaction of race and indicator for disadvantaged. * indicates significance at the 5% level. Marginal effects are
reported as percentage point values.

–

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 59 of 123

There is no consistent or statistically significant evidence of bias against Asian-American
applicants even when Prof. Arcidiacono’s preferred measures of extracurricular activity
participation are used

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 the
updated Card model with Prof. Arcidiacono’s preferred measures of extracurricular activity participation. * indicates significance at the 5%
level. Marginal effects are reported as percentage point values.

–

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 60 of 123

There is no consistent or statistically significant evidence of bias against Asian-American
applicants even when I modify my parental occupation variables to address Prof. Arcidiacono’s
critique

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 the
updated Card model with modifications to parental occupation controls, grouping ‘Laborer (Unskilled)’, ‘Low Skill’, ‘Self-Employed’,
‘Unemployed’, ‘Homemaker’, and ‘Other’ as one occupation category. * indicates significance at the 5% level. Marginal effects are reported
as percentage point values.

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 61 of 123

There is no consistent or statistically significant evidence of bias against Asian-American
applicants even if staff interview ratings are excluded from the model

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Table shows average marginal effect of race on admission for Asian-American applicants relative to White applicants using the
updated Card model removing the indicator for receiving a staff interview rating. * indicates significance at the 5% level. Marginal effects
are reported as percentage point values.

4.2. Analysis of key subgroups of the data provides further evidence that there is no bias in
Harvard’s admissions process

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 62 of 123

positive

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 63 of 123

The estimated effect of Asian-American ethnicity is positive (though statistically insignificant) for
Asian-American 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 the
updated Card model on the sample of female applicants. * indicates significance at the 5% level. Marginal effects are reported as
percentage point values.

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 64 of 123

The estimated effect of Asian-American ethnicity is positive (though statistically insignificant) for
Asian-American applicants from California

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 the
updated Card model on the sample of applicants applying from California dockets. * indicates significance at the 5% level. Marginal efects
are reported as percentage point values.

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 65 of 123

4.3. Prof. Arcidiacono’s new allegation of bias against dockets with larger shares of Asian-American
applicants lacks any causal credibility

all

not

positive
all

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 66 of 123

causal

no other docketspecific characteristics

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 67 of 123

Simple changes to Prof. Arcidiacono’s analysis of docket-level bias show that his allegations are not
credible

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: Docket-year fixed effects are obtained from Prof. Arcidiacono’s preferred admissions model estimated using applicants to the classes
of 2014 – 2019 who are in his expanded sample excluding athletes. Regressions of docket-year fixed effects on shares also contain year
fixed effects. * indicates significance at the 5% level.

only

all
in those same dockets

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 68 of 123

4.4. Other technical criticisms of my model do not change my findings

all

exactly
only upon
applicants who are not perfectly predicted

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 69 of 123

the most competitive applicants

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 70 of 123

multiple

5.1. Race alone is uninformative in Harvard’s decision process

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 71 of 123

paramount

once
we account for their other qualifications and/or life experiences

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 72 of 123

Race explains far less about admissions decisions than other key factors such as ratings

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 expanded sample including athletes. Predicted
probabilities are computed seperately each year, from which the pooled Pseudo R-Squared values are computed.

5.2. The fact that race has a relatively large effect on the probability of admissions for some
candidates cannot be taken as evidence that race is “determinative”

many

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 73 of 123

The effect of race follows the same pattern across deciles as other characteristics in Harvard’s
admissions process

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 expanded sample including athletes. Deciles are
constructed by year, across the full sample, based on the predicted probabilities of admission after removing the effect of the given
characteristic. Marginal effects are computed for applicants with the given characteristic relative to the baseline (i.e. White, non-lineage,
academic rating of 3, extracurricular rating of 3, and personal rating of 3). Marginal effects are reported as percentage point values. “-”
indicates that there are no applicants with a given characteristic in a given decile.

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 74 of 123

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 75 of 123

The effect of race is smaller than that of ratings for African-American applicants

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 expanded sample including athletes. Deciles are
constructed by year, across the full sample, based on the predicted probabilities of admission after removing the effect of race. All ratings
include the four profile ratings, teacher and guidance counselor ratings, and alumni ratings. Marginal effects are computed for AfricanAmerican applicants relative to the baseline (i.e. White, and ratings of 3 for applicants with ratings of 1 and 2). Marginal effects are
reported as percentage point values.

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 76 of 123

before

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 77 of 123

6.1. The record does not support Prof. Arcidiacono’s claim of a floor on single-race AfricanAmerican admissions starting with the class of 2017

before

purposely

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 78 of 123

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 79 of 123

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 80 of 123

6.2. The pattern that Prof. Arcidiacono claims as evidence of manipulation is not as unlikely as he
suggests

many

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 81 of 123

many

6.3. The relative quality of single-race African-American admitted students did not fall starting with
the class of 2017, further undermining the idea of a floor on their admission rate

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 82 of 123

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 83 of 123

None

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 84 of 123

The relative quality of single-race African-American admitted students did not fall in 2017
African-American
Admitted Students
Single-Race
Multi-Race
Admission Rate [2]
1. Average 2014 2016
2. Average 2017 2019
3. Difference-in-Difference

Difference [1]

6%
6%

10%
8%

-3% *
-2% *
2%

0.20
0.23

0.19
0.34

0.01
-0.12 *
-0.13

Fraction with Academic Rating of 1 or 2 [2]
5. Average 2014 2016
53%
6. Average 2017 2019
55%
7. Difference-in-Difference

48%
57%

5%
-2%
-7%

Fraction with Extracurricular Rating of 1 or 2 [2]
8. Average 2014 2016
47%
9. Average 2017 2019
48%
10. Difference-in-Difference

52%
49%

-5%
-1%
4%

Fraction with Personal Rating of 1 or 2 [2]
11. Average 2014 2016
74%
12. Average 2017 2019
74%
13. Difference-in-Difference

76%
72%

-2%
2%
4%

Fraction with Athletic Rating of 1 or 2 [2]
14. Average 2014 2016
20%
15. Average 2017 2019
22%
16. Difference-in-Difference

24%
28%

-5%
-6%
-1%

Average Admissions Index [4]
17. Average 2014 2016
18. Average 2017 2019
19. Difference-in-Difference

0.31
0.32

-0.07 *
-0.06 *
0.01

Average Academic Index [2][3]
4. Average 2014 2016
5. Average 2017 2019
6. Difference-in-Difference

0.24
0.26

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data
Note: [1] * indicates statistical significance at the 5% level. [2] Consistent with Prof. Arcidiacono's analyses, data are from domestic
admitted applicants, including prior admitted applicants and excluding deferred admitted applicants. [3] Academic Index values are in
standard deviation units. Average Academic Index calculations exclude students with GPA flags. [4] Data are from admitted applicants in
Prof. Arcidiacono’s expanded sample including athletes (my preferred year-by-year regression model sample). The admissions index is
constructed using applicants' predicted probability of admission after removing the effect of race.

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 85 of 123

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 86 of 123

Students for Fair Admissions, Inc. v. President and Fellows of
Harvard College (Harvard Corporation)

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 87 of 123

7.1. The academic literature establishes that race-neutral alternatives diminish selective universities’
ability to select on quality

greater
higher

The Future of Affirmative Action, ed. Richard Kahlenberg

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 88 of 123

Ohio St. Law Journal

Students for Fair Admissions, Inc. v. President
and Fellows of Harvard College (Harvard Corporation)

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 89 of 123

The Future of Affirmative
Action

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 90 of 123

could

could

exact same

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 91 of 123

7.2. Mr. Kahlenberg’s new simulations confirm that the substitution of race-neutral alternatives for
Harvard’s race-conscious admissions process would change the characteristics of the class and
compromise its quality

7.2.1. Mr. Kahlenberg’s criticisms of my simulations

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 92 of 123

not

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 93 of 123

not

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 94 of 123

7.2.2. The results of Mr. Kahlenberg’s new simulations support the conclusions of my first report

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 95 of 123

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 96 of 123

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 97 of 123

Kahlenberg’s Simulation 6 and 7: Impact on class characteristics
Predicted Class Without Consideration of Race and Factors that
Allegedly Advantage White Applicants
Card’s Simulation
(4x SES Boost)

Kahlenberg’s
Simulation 6

Kahlenberg’s
Simulation 7

Actual
Admitted
Class

Predicted
Value

% Change

Predicted
Value

% Change

Predicted
Value

% Change

Outcome Measures

[A]

[B]

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

[C]

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

[D]

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

1.
2.
3.
4.
5.

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

676
402
233
234
134

589
508
293
163
127

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

541
523
330
164
121

-20%
+30%
+42%
-30%
-10%

561
521
313
160
123

-17%
+30%
+34%
-32%
-8%

6.
7.
8.
9.

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

2244
33.1
77.0
228

2189
32.7
77.1
225

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

2173
32.5
77.0
225

-3%
-2%
+0.02%
-1%

2180
32.5
77.0
225

-3%
-2%
+0.02%
-1%

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

66%
57%
64%
18%

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

61%
54%
62%
20%

-19%
-13%
-13%
-26%

63%
55%
63%
21%

-17%
-12%
-11%
-22%

259

86

-67%

61

-76%

81

-69%

72

19

-73%

13

-81%

18

-75%

180

88

-51%

144

-20%

159

-11%

44

17

-61%

12

-74%

16

-64%

839

851

+1%

858

+2%

851

+1%

10.
11.
12.
13.

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.
19. Number of Female Students
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%
13%
23%
8%
13%
6%
7%
6%

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

24%
12%
24%
7%
14%
6%
6%
6%

-4%
-15%
+12%
-5%
+14%
-4%
+1%
-6%

24%
12%
24%
7%
14%
6%
6%
7%

-2%
-14%
+12%
-5%
+8%
-6%
+0.5%
-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

87
604
217
407
451

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

87
615
164
392
509

+47%
-11%
-21%
+3%
+27%

82
630
170
391
488

+39%
-9%
-18%
+3%
+22%

Source: Augmented Arcidiacono Data; College Board Cluster Data; U.S. Census Data; Kahlenberg Production

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 98 of 123

Note: My simulation (“Card’s Simulation (4x SES Boost)”) consists of applicants to the class of 2019 in Prof. Arcidiacono’s expanded
sample including athletes, who are in my preferred year-by-year regression model from my affirmative report. The 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 admissions index. The value is equal to 2 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.
Kahlenberg’s simulation 6 retains the same sample and regression model from my simulation. Simulation 6 eliminates consideration of
the same characteristics as my simulation except for recruited-athlete status. Simulation 6 also eliminates consideration of Early Action
status. Applicants with certain socioeconomic characteristics are given a low-SES boost by adding a value to their admissions index. The
value is equal to 1.6 multiplied by the number of characteristics an applicant displays out of the following: disadvantaged, requested a fee
waiver, first generation college student, applicant obtains a neighborhood SES index score in the bottom third of the distribution,
applicant obtains a high school SES index score in the bottom third of the distribution. The neighborhood and high school SES indices are
constructed by equally-weighting three standardized factors: parental income, parental education, and percentage of families speaking a
language other than English at home. Kahlenberg’s simulation 7 is the same as simulation 6 except that it retains consideration of Early
Action status.

7.3. Other race-neutral alternatives are unlikely to generate diversity without changing class
characteristics and compromising class quality

7.3.1. Increasing financial aid

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 99 of 123

lower

7.3.2. Increasing recruiting

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 100 of 123

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 101 of 123

7.3.3. Increasing transfer admissions

7.3.4. Eliminating deferred admission

All

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 102 of 123

7.3.5. Place-based admissions policies

7.4. Conclusion

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 103 of 123

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 104 of 123

8.1. Documents Relied Upon

Students for Fair Admissions, Inc. v. President and Fellows of
Harvard College (Harvard Corporation)
Students for Fair Admissions, Inc. v.
President and Fellows of Harvard College (Harvard Corporation)
Students for Fair
Admissions, Inc. v. President and Fellows of Harvard College (Harvard Corporation)
Students for Fair Admissions, Inc.
v. President and Fellows of Harvard College (Harvard Corporation)
Students for Fair
Admissions, Inc. v. President and Fellows of Harvard College (Harvard Corporation)

Journal of Labor Economics
Economics of Education Review

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 105 of 123

Sociology of Education
Journal of Econometrics
Industrial and Labor
Relations Review
Journal of Labor Economics
The American
Economic Review

Econometrica

International Economic Review
American
Economic Review: Papers & Proceedings

Southern Economic Journal
Ohio St. Law Journal

The Future of Affirmative Action

Affirmative Action for the Rich

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 106 of 123

Handbook of Labor Economics, Volume 4A

Socioeconomic Status, Parenting, and Child Development

Introduction to Econometrics
The
Future of Affirmative Action
Occupations and Social
Status
Race, Class, and Affirmative Action

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 107 of 123

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 108 of 123

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 109 of 123

9.1. Appendix B.1 Constructing categories for parental occupations

Handbook of Labor Economics, Volume 4A

Journal of Labor Economics
Journal of Labor Economics

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 110 of 123

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 111 of 123

My results are robust to changes in occupational classifications

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 previously defined expanded sample. * indicates significance at the 5% level. Marginal effects are reporteed as percentage
point values.

Construction of occupational categories

Card Category

BLS Major
or Minor
Group

0

Other

-

Includes 99-0004, Undecided; 99-0002
and 00-0003, Retired; 99-0003 and 000004, Other; or missing

-

1

Homemaker

-

Includes 00-0001, Homemaker

Includes 2010-21, Homemaker
(full-time)

2

Unemployed

-

Includes 99-0001 and 00-0002,
Unemployed; 99-0005, Disabled

-

3

Skilled Trades
Incl.
Construction and
Extraction

47, 49, 51

Includes 47, Construction and Extraction;
49, Installation, Maintenance and Repair;
51, Production

Includes 2010-42, Skilled
Trades; 2010-44, Semi-Skilled
Worker; 2010-43, Laborer
(unskilled)

Includes 35, Food Preparation and
Serving; 53, Transportation and Material
Moving; 37, Building and Grounds
Cleaning and Maintenance; 45, Farming,
Fishing and Forestry; 31, Healthcare
Support; 39, Personal Care and Service

Includes 2010-15,
Conservationist or Forester

4

Low Skill
Occupations

35, 53,
37, 45, 31,
39

5

Self-Employed

-

BLS, "99-XXXX", and "00-XXXX"
Codes

“2010-XX” Codes

Includes 2010-07, Business
Owner or Proprietor

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 112 of 123

Card Category

6

7

8

Business
Executive
(management,
administrator)
Other
Management
Occupations
(Excl. Business
Execs)
Business and
Financial
Operations
Occupations

BLS
Major
or
Minor
Group

BLS, "99-XXXX", and "00-XXXX"
Codes

“2010-XX” Codes

11-1, 11-2,
11-3

Includes 11-1, Top Executives; 11-2
Advertising, Marketing, Promotions,
Public Relations, and Sales Managers; 11-3,
Operations Specialties Managers

Includes 2010-06, Business
Executive (management,
administrator); 2010-20, Foreign
Service Worker (including
diplomat); 2010-32,
Policymaker/Government

11-9

Includes 11-9, Other Management
Occupations

Includes 2010-34, School
Principal or Superintendent;
2010-12, College Administrator
or Staff

13

Includes 13, Business and Financial
Operations

Includes 2010-01, Accountant or
Actuary

9

Computer and
Mathematical
Occupations

15

Includes 15, Computer and Mathematical

Includes 2010-14, Computer
Programmer or Analyst

10

Architecture and
Engineering
Occupations

17

Includes 17, Architecture and Engineering

Includes 2010-18, Engineer;
2010-03, Architect or Urban
Planner

19

Includes 19, Life, Physical, and Social
Science

Includes 2010-35, Scientific
Researcher; 2010-11, Clinical
Psychologist

21

Includes 21, Community and Social
Services Occupations

Includes 2010-09, Clergy
(minister, priest); 2010-10,
Clergy (other religious); 2010-33,
School Counselor; 2010-36,
Social, Welfare, or Recreation
Worker

23-1

Includes 23-1, Lawyers, Judges, and
Related Workers

Includes 2010-25, Lawyer
(attorney) or Judge

25-1

Includes 25-1, Postsecondary Teachers

Includes 2010-13, College
Teacher
Includes 2010-38, Teacher or
Administrator (elementary);
2010-39, Teacher or
Administrator (secondary)

11

12

13

Life, Physical,
and Social
Science
Occupations
Counselors,
Social Workers,
and Other
Community and
Social Service
Specialists
Lawyers, Judges,
and Related
Workers

14

Postsecondary
Teachers

15

Pre-K through
Grade 12
Educational
Instruction and
Library
Occupations

25-2, 253, 25-4,
25-9

Includes 25-2, Preschool, Primary,
Secondary, and Special Education School
Teachers; 25-3, Other Teachers and
Instructors; 25-4, Librarians, Curators,
and Archivists; 25-9, Other Education,
Training, and Library Occupations

16

Entertainers and
Performers,
Sports and
Related Workers

27-2

Includes 27-2, Entertainers and
Performers, Sports and Related Workers

Includes 2010-02, Actor or
Entertainer; 2010-27, Musician
(performer, composer)

17

Arts, Design, and
Media Workers

27-1, 273, 27-4

Includes 27-1, Art and Design Workers; 273, Media and Communication Workers;
27-4, Media and Communication
Equipment Workers

Includes 2010-04, Artist; 201022, Interior Decorator (including
designer); 2010-41, Writer or
Journalist

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 113 of 123

Card Category

18

Health
Diagnosing and
Treating
Practitioners

BLS
Major
or
Minor
Group
29-1
(excludin
g 291070, 291140, 291150, 291160, 291170)

BLS, "99-XXXX", and "00-XXXX"
Codes

“2010-XX” Codes

Includes 29-1, Health Diagnosing and
Treating Practitioners, except for 29-1070,
Physician Assistants; 29-1140, Registered
Nurses; 29-1150, Nurse Anesthetists; 291160, Nurse Midwives; and 29-1170, Nurse
Practitioners

Includes 2010-16, Dentist
(including orthodontist); 201031, Physician; 2010-37, Therapist
(physical, occupational, speech)

Includes 29-2, Health Technologists and
Technicians; 29-9, Other Healthcare
Practitioners and Technical Occupations;
29-1070, Physician Assistants; 29-1140,
Registered Nurses; 29-1150, Nurse
Anesthetists; 29-1160, Nurse Midwives;
and 29-1170, Nurse Practitioners

Includes 2010-28, Nurse; 201023, Lab Technician or Hygienist

19

Other Healthcare
Occupations Incl.
Nurses

29-2, 299, 291070, 291140, 291150, 291160, 291170

20

Protective Service
Occupations

33

Includes 33, Protective Service
Occupations

Includes 2010-24, Law
Enforcement Officer

21

Sales and Related
Occupations

41

Includes 41, Sales and Related Occupations

Includes 2010-08, Business
Salesperson or Buyer

22

Office and
Administrative
Support
Occupations

43, 23-2

Includes 43, Office and Administrative
Support Occupations; and 23-2, Legal
Support Workers

Includes 2010-05, Business
(clerical)

23

Military Specific
Occupations

55

Includes 55, Military Specific Occupations

Includes 2010-26, Military
service (career)

Source: Augmented Arcidiacono Data
Note: BLS, “99-XXXX”, and “00-XXXX” codes are used by applicants to the classes of 2014 – 2019, and is the only code used by applicants
to the class of 2014. "2010-XXXX” codes are used by applicants to the classes of 2015 – 2019, and are used by the majority of applicants to
the classes of 2015 – 2019.

9.2. Appendix B.2: Error in Prof. Arcidiacono’s difference-in-difference estimates

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 114 of 123

9.3. Appendix B.3: Using absolute deviation to measure the importance of unobserved
characteristics is appropriate

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 115 of 123

Journal of Econometrics
Econometrica

International Economic
Review

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 116 of 123

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 117 of 123

10.1. List of variables included in model of admission

Variable Name

Variable Description

Race Variables

race

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.”

racecoll

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.

Base Controls
year
female

Harvard class to which applicant applies: 2014 to
2019.
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

Indicator for whether at least one of applicant’s
parents attended Harvard.

double_legacy

Indicator for whether both of applicant’s parents
attended Harvard.

faculty_or_staff_kid

Indicator for whether applicant is child of Harvard
faculty and staff.

deanDirectorPref

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

meduc

Categories for mother’s level of education: “Less
than college,” “College graduate,” “Master’s,”
“MD/JD/PhD,” “Missing.”

feduc

Categories for father’s level of education: “Less
than college,” “College graduate,” “Master’s,”
“MD/JD/PhD,” “Missing.”

intendedMajor

Categories for applicant’s intended major: “Social
sciences,” “Humanities,” “Biological sciences,”
“Physical sciences,” “Engineering,” “Mathematics,”
“Computer Sciences,” “Unspecified.”

docketFE

Docket to which applicant’s high school is assigned.

Constructed
by
Arcidiacono

Card
Initial
Model

Card
Updated
Model

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 118 of 123

Variable Name

Variable Description

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

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.

teach_combos

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.

counslor_rat_abbr

School support rating, assigned by Admissions
Committee, based on applicant’s recommendation
from guidance counselor. Categories: 1, 2, 3, 4, 5,
and Missing.

alum_combos

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.

Constructed
by
Arcidiacono

Card
initial
model

Card
updated
model

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 119 of 123

Variable Name

Variable Description

Ratings Variables (Continued)
Academic profile rating with categories:
academic_rat_abbr
1, 2, 3, 4, 5 and 6.
Personal profile rating with categories:
personal_rat_abbr
1, 2, 3, 4, 5 and 6.
xtracurr_rat_abbr
athletic_rat_abbr

Extracurricular profile rating with categories:
1, 2, 3, 4, 5 and 6.
Athletic profile rating with categories:
1, 2, 3, 4, 5 and 6.

alum1_rat_abbr

Alumni interview personal rating with categories: 1,
2, 3, 4, 5 or 6, and Missing.

alum2_rat_abbr

Alumni interview overall rating with categories:
1, 2, 3, 4, 5 or 6, and Missing.

m_alum_rat

Indicator for missing alumni interviewer ratings.

rat2_*

Indicators for having ratings of 2 or better for each
pair of profile ratings (e.g. academic and personal,
athletic and extracurricular, etc.).

teacher1_rat_abbr

School support rating, assigned by Admissions
Committee, based on applicant’s recommendation
from Teacher 1. Categories: 1, 2, 3, 4, 5, and
Missing.

teacher2_rat_abbr

School support rating, assigned by Admissions
Committee, based on applicant’s recommendation
from Teacher 2. Categories: 1, 2, 3, 4, 5, and
Missing.

alum_twos
school_twos

Count of alumni interview ratings (personal and
overall) of 2 or better.
Count of school support ratings (teacher 1, teacher
2, and guidance counselor) of 2 or better.

Constructed
by
Arcidiacono

Card
initial
model

Card
updated
model

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 120 of 123

Variable Name
Contextual Factors

Variable Description

father_occ_cat

Mother’s occupation category

mother_occ_cat

Father’s occupation category

father_deceased_yn
mother_deceased_yn

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

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.

intendedCareer

Intended career indicated by applicant, from a
choice of 15 career categories, "Other,"
"Undecided," or "Unknown."

school_type

School type (public, private, Catholic, or missing)

legacy_grad

Indicator for whether at least one of applicant’s
parents went to Harvard Graduate School.

perm_res
total_work

Indicator for whether applicant is a United States
permanent resident.
Total hours of work reported in activity
description.

primcoll_*

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.

r_staff_yn

Indicator for whether applicant received a staff
interview rating.

born_USA
outside_US_yn

Indicator for whether applicant was born outside of
United States.
Indicator for whether applicant lived outside of
United States.

Constructed
by
Arcidiacono

Card
initial
model

Card
updated
model

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 121 of 123

Variable Name

Variable Description

Constructed
by
Arcidiacono

Card
initial
model

Card
updated
model

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
sat_state
Harvard (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
hs_sat_math
all students at applicant’s high school.
hs_sat_cr
hs_sat_w

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

Average # of AP tests taken by students at
applicant’s high school.

hs_fin_aid
hs_avg_hon

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.

hs_parent_ed

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

hs_avg_sat_sends

Average number of scores sends for students at
applicant’s high school.

hs_coll_admit_rate

Average rate of admission for colleges receiving
score sends from students at applicant’s high
school.

hs_black†

ACS-based percent of students at applicant’s high
school who are Black.

hs_white†

ACS-based percent of students at applicant’s high
school who are White.

hs_hispanic†

ACS-based percent of students at applicant’s high
school who are Hispanic.

hs_med_income†
hs_pov_line†
hs_house_val†

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.

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 122 of 123

Constructed
by
Arcidiacono

Card
initial
model

Card
updated
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.
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

Percent of students in applicant’s neighborhood
who applied to an out of state college.

n_avg_num_ap

Average # of AP tests taken by students in
applicant’s neighborhood.

n_fin_aid

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†

ACS-based median family income of students in
applicant’s neighborhood, missing values filled
with mean.

n_pov_line_imp†

ACS-based percent of students in applicant’s
neighborhood who are below the poverty line,
missing values filled with mean.

n_house_val_imp†

ACS-based median value of home for students in
applicant’s neighborhood, as a percentage of
average state value, missing values filled with
mean.

Case 1:14-cv-14176-ADB Document 421-253 Filed 06/15/18 Page 123 of 123

Variable Name
m_n_pov_line†
m_n_med_income†
m_n_house_val†

Variable Description

Constructed
by
Arcidiacono

Card
initial
model

Card
updated
model

Indicator for missing neighborhood poverty line
variable.
Indicator for missing neighborhood median income
variable.
Indicator for missing neighborhood house value
variable.

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.