# Amicus Curiae Brief — Fisher v. Univ. of Tex. At Austin, 135 S. Ct. 2888 (2015) (No. 14-981)

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URL: https://www.frixlaw.com/law-library/documents/brief%3Amicro_IA40385016_0328%3A051

## Record

- **Collection:** Supreme Court brief
- **Document type:** Amicus Curiae Brief
- **Published:** January 1, 2015

## Text

No. 14-981

IN THE

Supreme Court of the Anited States

ABIGAIL NOEL FISHER,
Petitioner,

Vv.

UNTVERSITY OF TEXAS AT AUSTIN, ET AL.,
Respondents.

On Writ of Certiorari to the
United States Court of Appeals
for the Fifth Circuit

BRIEF OF EMPIRICAL SCHOLARS
AS AMICI CURIAE
IN SUPPORT OF RESPONDENTS

IVAN ESPINOZA-MADRIGAL A.W. PHINNEY III

OREN M. SELLSTROM Counsel of Record
MATTHEW CREGOR YALONDA T. HOWZE
LAWYERS’ COMMITTEE COLIN G. VAN DYKE
FOR CIVIL RIGHTS AND MATHILDA S. MCGEE-TUBB
ECONOMIC JUSTICE MINTZ, LEVIN, COHN, FERRIS,
294 Washington Street GLOVSKY AND POPEO, P.C.
Suite 443 One Financial Center
Boston, MA 02108 Boston, MA 02111
(617) 482-1145 (617) 542-6000
AWPhinney@Mintz.com

November 2, 2015

a ooo
WILSON-EPES PRINTING CO, INC. — (202) 789-0096 — WasHinGcTon, D.C. 20002

TABLE OF CONTENTS

TABLE OF AUTHORITIES. ..................ccc:ccseee00
INTEREST OF AMICI CURIAE .............00000000085
PREFATORY STATEMENT ..................:00ceeeeeeees
SUMMARY OF ARGUMENT ................0::c0eceeeees

A. “Mismatch” Hypothesis In Brief ..............

B. Sander’s “Mismatch” Research Does Not
Represent A Consensus In Social Science...

C. The “Mismatch” Research Violates Basic
Principles Of Causal Inference.................

1.
2.

3.

SE eee ee nae

Research Principles For Causal
ITT siisuiducniesiabalitsbaintepanaieebnansingiebaiuaann

Methodological Flaws.........................
a. Invalid Comparisons ......................

b. Adjusting For Pre-Existing
Characteristics .................ccccsceseeees

. EERE EE SUE SC PREC ee oN Te

(i)

Page

25
27

il
TABLE OF AUTHORITIES

CASES Page(s)

Daubert v. Merrill Dow Pharm.,
I ccecanice

Fisher v. University of Texas at Austin,

16

133 S. Ct. 2411 (2013) ...............eeecccceeeeeeeeees passim

Grutter v. Bollinger,
539 U.S. 328 (ZOOS) .............cesseseceeceeeeeeeseees

Regents of Univ. of Cal. v. Bakke,
Se Cy HP IED esccoccccsteccscesspnosocesaceseoseces

MISCELLANEOUS

Sigal Alon & Marta Tienda, Assessing the
“Mismatch” Hypothesis: Differences in
College Graduation Rates by Institutional
Selectivity, 78 Soc. Educ. 294 (20085)............

Ian Ayres & Richard Brooks, Does Affirmative
Action Reduce the Number of Black Lawyers?,
57 Stan. L. Rev. 1807 (2005)..................0.00+-

William G. Bowen et al., Crossing the Finish
Line: Completing College at America’s Public
Universities (Princeton Univ. Press 2011).....

William G. Bowen & Derek Bok, The Shape of
the River (Princeton Univ. Press 2000) .......

Gregory Camilli et al., The Mismatch Hypoth-
eses in Law School Admissions, 2 Widener
J.L. Econ. & Race 165 (2011).................0.....

14

12

13

iii
TABLE OF AUTHORITIES—Continued
Page(s)

David L. Chambers et al., The Real Impact of
Eliminating Affirmative Action in American
Law Schools: An Empirical Critique of
Richard Sander’s Study, 57 Stan. L. Rev.
aac eter eel cinenainiiebdinmennietie 9,13

Kalena E. Cortes, Do Bans on Affirmative
Action Hurt Minority Students? Evidence
from the Texas 10% Plan, 29 Econ. Educ.
EEE RETENE SEES SIS oe nna 14

Stacy Berg Dale & Alan B. Krueger, Estimat-
ing the Payoff to Attending a More Selective
College: An Application of Selection on
Observabies and Unobservables, 117 Q.J.
ATL EE ene 25

Stacy Dale & Alan B. Krueger, Estimating the
Return to College Selectivity over the Career
Using Administrative Earnings Data (Nat'l
Bureau of Econ. Research, Working Paper
No. 17159, June 2011), available at http://

www.nber.org/papers/w17159...................... 25, 26
Michele Landis Dauber, The Big Muddy, 57
Stan L. Rev. 1899 (2005)..................cecceeeeeees 13

Rogers Elliott et al., The Role of Ethnicity in
Choosing and Leaving Science in Highly
Selective Institutions, 37 Res. Higher Educ.
asia iain ere erciamiemidnnssdinnetn 11

Mary J. Fischer & Douglas S. Massey, The
Effects of Affirmative Action in Higher
Education, 36 Soc. Sci. Res. 531 (2007)....... 14

iv
TABLE OF AUTHORITIES—Continued
Page(s)

Constantine Frankgakis & Donald B. Rubin,
Principal Stratification in Causal Inference,
58 Biometrics 21 (2002).....................cccceceeees 22

Andrew Gelman & Jennifer Hill, Data Analysis
Using Regression and Multilevel/ Hierar-
chical Models (Cambridge Univ. Press
SEERA ES Ra ae 22

Cheryl I. Harris & William C. Kidder, The
Black Student Mismatch Myth in Legal
Education: The Systemic Flaws in Richard
Sander’s Affirmative Action Study, J. Blacks
Higher Educ. (2005) ....................::::ssseeeeseees 13

Daniel E. Ho, Affirmative Action’s Affirmative
Actions: A Reply to Sander, 114 Yale L.J.
SITS iildidtisiesnsiniteiatiateedaleninganseneeeanonmiant 13

Daniel E. Ho, Why Affirmative Action Does
Not Cause Black Students to Fail the Bar,
114 Yale L.J. 1997 (2005)............................. 10, 24

Daniel E. Ho et al., Matching as Nonparametric
Preprocessing for Reducing Model Depend-
ence in Parametric Causal Inference, 15 Pol.

ee ee icictinntnsitnicimnemancriimniniinen 22
Paul W. Holland, Statistics and Causal Infer-
ence, 81 J. Am. Stat. Ass’n 945 (1986)......... 18

Guido W. Imbens & Donald B. Rubin, Causal
Inference in Statistics and Social Sciences
(Cambridge Univ. Press 2015)............... 19, 21, 22

Vv

TABLE OF AUTHORITIES—Continued

Page(s)

Thomas J. Kane, Racial and Ethnic Preferences
in College Admissions, 59 Ohio St. L.J. 971
SN SERIE SEIS SED Sn ate ee eT

Mark C. Long, College Quality and Early
Adult Outcomes, 27 Econ. Educ. Rev. 588

Tatiana Melguizo, Are Students of Color More
Likely to Graduate from College If They
Attend More Selective Institutions? Evidence
from a Cohort of Recipients and Nonrecipi-
ents of the Gates Millennium Scholarship
(GMS) Program, 32 Educ. Eval. & Pol’y
I ciiiittcniaswersccseensmnennininnses

Tatiana Melguizo, Quality Matters: Assessing
the Impact of Attending More Selective
Institutions on College Completion Rates of
Minorities, 49 Res. Higher Educ. 214 (2008) ..

Beverly I. Moran, The Case for Black Inferior-
ity? What Must Be True If Professor Sander
Is Right: A Response to A Systemic Analysis
of Affirmative Action in American Law
Schools, 5 Conn. Pub. Int. L.J. 41 (2005) ....

Angela Onwuachi-Willig & Amber Fricke,
Class, Classes, and Classic Race-Baiting:
What’s in a Definition?, 88 Denv. U.L. Rev.
ER i, RIE TR NEN NA

14

15

15

15

13

14

vi
TABLE OF AUTHORITIES—Continued

Page(s)

Paul R. Rosenbaum, The Consequences of
Adjustment for a Concomitant Variable
That Has Been Affected by the Treatment,
147 J. Royal Stat. Soc’y Series A (Gen.) 656
IIIc itiiceieiiehccibcicanenhdeniidadaiencntiineeiniedbiainanee

Jesse Rothstein & Albert Yoon, Affirmative
Action in Law School Admissions: What Do
Racial Preferences Do?, 75 U. Chi. L. Rev.
ELE EE Oe ee

Donald B. Rubin, Bayesian Inference for Causal
Effects: The Role of Randomization, 6 Annals
ee eiseciceniiimicieisamineieiiitdaiidnibentaiestense

Donald B. Rubin, Causal Inference Through
Potential Outcomes and Principal Stratifi-
cation: Applications to Studies with “Cen-
soring” Due to Death, 21 Stat. Sci. 299
III ietstiesinnecitasieneshanmeuiedihadeiandaiaaignindaduuadunenessiies

Donald B. Rubin, The Design Versus the Anal-
ysis of Observational Studies for Causal
Effects: Parallels with the Design of Ran-
domized Trials, 26 Stat. Med. 20 (2007)........

Donald B. Rubin, Estimating Causal Effects
of Treatments in Randomized and Nonran-
domized Studies, 66 J. Educ. Psychol. 688
ETE icarenatibibaniennndunedcuianeineainanipnbiinmsnniidtinitenies

Donald B. Rubin, For Objective Causal Infer-
ence, Design Trumps Analysis, 2 Annals
Applied Stat. 808 (2008)............0............e.

22

15

18

22

19

19

19

vii
TABLE OF AUTHORITIES—Continued
Page(s)

Richard H. Sander, A Systemic Analysis of
Affirmative Action in American Law Schools,
57 Stan. L. Rev. 367 (2004) ........................ passim

Richard H. Sander, Class in American Legal
Education, 88 Denv. U. L. Rev. 631 (2011).. 14

Richard H. Sander, Mismeasuring the Mis-
match: A Response to Ho, 114 Yale L.J.
RR ER PLE Re RE emerson me 20

Richard H. Sander & Jane Yakowitz Bambauer,
The Secret of My Success: How Status,
Prestige and School Performance Shape Legal
Careers (UCLA School of Law Research
Paper No. 10-26, July 2010), available at
http://papers.ssrn.com/
8o0l3/papers.cfm?abstract_id=1640058......... 23

Richard Sander & Roger Bolus, Do Credential
Gaps in College Reduce the Number of
Minority Science Graduates? (Project SEA-
PHE Working Paper, July 2009), available
at http://www.seaphe.org/working-paperw’.... 12

Mario L. Small & Christopher Winship, Black
Students’ Graduation from Elite Colleges:
Institutional Characteristics and Between-
Institution Differences, 36 Soc. Sci. Res.
GEER AE IAS Ee Pee en 15

Thomas Sowell, Black Education: Myths and
Tragedies (David McKay 1972).................... 11

viii
TABLE OF AUTHORITIES—Continued
Page(s)

U.S. News and World Report, Best Law
Schools, http://gradschools.usnews.rankings
andreviews.com/best-graduate-schools/top-
law-schools/law-rankings (last visited Octo-

I Ts SE kncecnssccenscncédssedbdenidnanmhiameiamamiameies 21
Linda F. Wightman, User’s Guide: LSAC
National Longitudinal Data File (1999)...... 21

Doug Williams, Does Affirmative Action Create
Educational Mismatches in Law Schools?
(Working Paper, Apr. 2009), available at
http://econ.duke.edu/~hf14/ERID/Williams.

ST seccenopinientibneemstianicga iscadaiaeae 15, 18, 20, 22

Alice Xiang & Donald B. Rubin, Assessing the
Potential Impact of a Nationwide Class-
Based Affirmative Action System, 30 Stat.

BEL Se CIEE? siccrncocevsccestnscenseininnmenenmanainias 8

Junni Zhang & Donald B. Rubin, Estimation
of Causal Effects via Principal Stratifica-
tion When Some Outcomes Are Truncated
by “Death,” 28 J. Educ. & Behav. Stat. 353

INTEREST OF AMICI CURIAE'

Amici curiae are leaders in the field of quantitative
social science and statistical methodology. Tiey file
this brief in order to point out to the Court the sub-
stantial methodological flaws in the research discussed
in the Brief Amicus Curiae for Richard Sander in
Support of Neither Party. Based on over 274 collective
years of social science research experience, amici have
concluded that the research on which that brief relies,
Professor Sander’s “mismatch” hypothesis, is unrelia-
ble, failing basic tenets of research design.

Guido Imbens is a Professor of Economics at the
Graduate School of Business at Stanford University.
He held positions in the Economics Departments at
University of California Berkeley, UCLA, and Harvard
University before joining Stanford in 2012. His work
has been supported by the National Science Foundation,
and he is a fellow of both the Econometric Society and
the American Academy of Arts and Sciences. His
primary field of interest is econometrics, and he has
conducted influential research on a broad range of issues
throughout the social sciences, greatly improving social
scientists’ ability to assess the causal effects of inter-
ventions from both field and experimental data. He
also works with governments and policy institutions
on designing and evaluating economic policy interven-
tions in aveas such as education and labor. Along with

‘ No counsel for a party authored this brief in whole or part,
and no counsel or party made a monetary contribution to fund the
preparation or submission of this brief. No person other than the
amici curiae and their counsel made any monetary contribution
to its preparation and submission. The parties have consented to
this filing.

2

Professor Rubin, he is the co-author of a widely
acclaimed textbook on principles for causal inference.

Donald B. Rubin is the John L. Loeb Professor of
Statistics at Harvard University, where he served as
chairman for thirteen years of his three decades there
as full Professor of Statistics. He has authored nearly
400 publications (among them several books), including
pioneering work on causal inference in experiments
and observational studies. His publications have gen-
erated nearly 160,000 citations. He is a Fellow of
the American Statistical Association, the Institute for
Mathematical Statistics, the International Statistical
Institute, the Woodrow Wilson Society, the John Simon
Guggenheim Society, the New York Academy of Sciences,
the American Association for the Advancement of Sci-
ences, the American Academy of Arts and Sciences,
and the Alexander von Humboldt Foundation. He
is also the recipient of four of the most prestigious
awards available to statisticians: the Samuel S. Wilks
Medal of the American Statistical Association, the
Parzen Prize for Statistical Innovation, the Fisher
Lectureship, and the George W. Snedecor Award of the
Committee of Presidents of Statistical Societies. He is
an Elected Member of the U.S. National Academy of
Sciences and an Elected Fellow of the British Academy.
Furthermore, he is the recipient of many other awards
and honors, including an Honorary Doctorate from
the Faculty of Social Sciences and Economics, Otto
Friedrich University, Bamberg, Germany.

Gary King is the Albert J. Weatherhead III University
Professor at Harvard University—one of only twenty-
four with the title of University Professor, Harvard’s
most distinguished faculty position. He is based in the
Department of Government (in the Faculty of Arts and
Sciences) and serves as Director of the Institute for

3

Quantitative Social Science. He has been elected Fellow
in eight honorary societies: National Academy of Sci-
ences (2010), National Academy of Social Insurance
(2014), American Statistical Association (2009), Amer-
ican Association for the Advancement of Science
(2004), American Academy of Arts and Sciences (1998),
Society for Political Methodology (2008), American
Academy of Political and Social Science (2004), and the
Guggenheim Foundation (1994-95). He has won more
than forty “best of awards for his work, including
leading contributions to the statistics of causal infer-
ence. He was listed as the most cited political scientist
of his cohort. His research has been supported by the
National Science Foundation, the Centers for Disease
Control and Prevention, the World Health Organization,
the National Institute of Aging, the Global Forum for
Health Research, as well as numerous centers, corpo-
rations, foundations, and other federal agencies.

Richard A. Berk is a Professor of Statistics and
Criminology at the University of Pennsylvania, where
he has appointments in the Department of Statistics
in the Wharton School and in the Department of Crim-
inology in the School of Arts and Sciences. He was pre-
viously a Distinguished Professor of Statistics at UCLA,
a Professor of Statistics and Sociology at University of
California, Santa Barbara, and a Professor of Sociology
at Northwestern University. He has held visiting
appointments at the Ecole Normale Supérieure in
Paris in the Department of Earth, Atmosphere and
Oceans, and with the Statistics Group of the Los
Alamos National Laboratories. He has authored over
200 publications (among them 15 books), including
highly respected work on causal inference, regression
analysis, machine learning, and forecasting. He has
been elected to the Sociological Research Association,
and is an Elected Fellow to the American Association

4

for the Advancement of Science, the American Statistical
Association, and the Academy of Experimenta! Crimi-
nology. He has received the Paul S. Lazarsfeld Award
for methodological contributions from the American
Sociological Association. He is a founding editor of
The Evaluation Review, a journal of applied social
research. Professor Berk received his B.A. from Yale
University and his Ph.D. from The Johns Hopkins
University.

Daniel E. Ho is the William Benjamin Scott and
Luna M. Scott Professor of Law at Stanford Law
School, where he teaches statistics and administrative
law. His scholarship centers on quantitative empirical
legal studies, and he holds a J.D. from Yale Law
School and a Ph.D. in Political Science from Harvard
University. He was the Maurice R. Greenberg Visiting
Professor of Law at Yale Law School and a Visiting
Professor of Law at New York University School of
Law. His work has been supported by the National
Science Foundation, and his work has been awarded
numerous prizes, including the Warren Miller Prize
(for an article on causal inference), awarded to the best
paper published in Political Analysis. He served as
the President of the Society for Empirical Legal Stud-
ies (2011-12) and is co-editor of the Journal of Law,
Economics, & Organization.

Kevin M. Quinn is a Professor of Law at the UC
Berkeley School of Law (Boalt Hall). Previously, he
has served as Associate Professor of Government at
Harvard University, and as Assistant Professor of
Political Science and Adjunct Assistant Professor of
Statistics at the University of Washington. He has
written extensively on statistical methodology, and he
teaches courses on applied statistics and empirical
legal studies. He is a three-time winner of the Gosnell

5

Prize for excellence in political methodology, and his
research has been supported by the National Science
Foundation. He served as Associate Editor for the
Journal of the American Statistical Association and as
President of the Society for Political Methodology.

D. James “Jim” Greiner is the William Henry
Bloomberg Professor of Law at Harvard Law School.
His research focuses on the development of rigorous
quantitative methods, with a particular focus on causal
inference in observational and experimental settings.
His work has been widely published in law reviews
(such as the Harvard Law Review and the Yale Law
Journal), as well as in peer-reviewed statistics journals
(the Review of Econom ‘cs and Statistics, the Journal
of the Royal Statistica: ssociation, and the Annals of
Applied Statistics). He teaches courses on civil proce-
dure, voting rights, and expert witnesses. He holds a
J.D. from the University of Michigan Law School and
a Ph.D. in Statistics from Harvard University.

Ian Ayres is the William K. Townsend Professor at
Yale Law School, the Anne Urowsky Professorial Fellow
in Law, and a Professor at Yale’s School of Manage-
ment. He has published 11 books and over 100 articles
on a wide range of topics, including leading work in
empirical legal studies. In 2006, he was elected to the
American Academy of Arts and Sciences. He has been
ranked as one of the most prolific and most-cited law
professors of his generation. He previously taught at
the law schools of Harvard University, University of
Illinois, Northwestern University, Stanford University,
and University of Virginia and served as a research
fellow of the American Bar Foundation. From 2002
to 2009, he was the editor of the Journal of Law,
Economics, & Organization. He holds a J.D. from Yale
Law School and a Ph.D. in Economics from M.I.T.

6

Richard Brooks is the Charles Keller Beekman Pro-
fessor of Law at Columbia Law School. Prior to joining
the Columbia Law School faculty in July 2013, he was
the Leighton Homer Surbeck Professor of Law at Yale
Law School. He previously taught at Cornell University
and Northwestern University. His scholarship centers
on law and economics, often involving empirical com-
ponents. He holds a J.D. from the University of Chicago
and a Ph.D. in economics from University of California
Berkeley.

Paul Oyer is the Fred. H. Merrill Professor of Eco-
nomics at Stanford University’s Graduate School of
Business, where he teaches the core Human Resources
Management class ir the M.B.A. program as well as a
Ph.D. class in Personnel Economics. Before moving to
Stanford in 2000, he was on the faculty of the Kellogg
School of Management at Northwestern University. In
his pre-academic life, he worked for the management
consulting firm of Booz, Allen, and Hamilton, as well
as for 3Com Corporation and ASK Computer Systems.
He holds a B.A. in Mathematics and Computer Science
from Middlebury College, an M.B.A. from Yale Univer-
sity, and an M.A. and a Ph.D. in Economics from
Princeton University.

John J. Donohue III is the C. Wendell and Edith M.
Carlsmith Professor of Law at Stanford Law School.
He has been one of the leading empirical researchers
in the legal academy over the past 25 years. He is
well-known for using empirical analysis to determine
the impact of law and public policy in a wide range of
areas, including civil rights and antidiscrimination law,
employment discrimination, crime and criminal justice,
and school funding. Previously, he was on the faculty
of Yale Law School and Northwestern University
School of Law, and served as a research fellow with the

7

American Bar Foundation. He is a member of the
American Academy of Arts and Sciences. He has pre-
viously served as the editor of the American Law and
Economics Review and president of the American Law
and Economics Association.

PREFATORY STATEMENT

When this case was last before this Court, Professor
Richard H. Sander, along with lawyer and journalist
Stuart S. Taylor, Jr., filed a brief amici curiae arguing
that social-science research has shown affirmative action
to be harmful to minority students, based on Professor
Sander’s so-called “mismatch” hypothesis. See Fisher
v. University of Texas at Austin, Docket 11-345 (Fisher J),
Brief Amici Curiae for Richera Sander and Stuart
Taylor, Jr. in Support of Neither Party (“Sander Fisher I
Brief”). Sander has filed a similar brief in the present
appeal, in large part repeating the same arguments he
made in Fisher I. See Fisher v. University of Texas at
Austin, Docket 14-981 (Fisher II), Brief Amicus Curiae
for Richard Sander in Support of Neither Party
(“Sander Fisher II Brief’).

In Fisher I, amici filed a brief amici curiae demon-
strating that Sander’s “mismatch” research rests on
significant methodological flaws, a brief that Sander
has described as “among legal educators, probably the
best known of ali critiques against the mismatch
hypothesis. ' Sander Fisher I] Brief at 27.

Amici stand fully behind the arguments they made
and the conclusions they drew in their brief in Fisher J
and reproduce it below for the Court’s convenience.’

? Citations below are to the Sander Fisher I Brief, for ease of
reference, amici nave added citations to the Sander Fisher IJ
Brief where the same arguments are made. As noted, while the

8

eee

SUMMARY OF ARGUMENT

In Grutter v. Bollinger, this Court held that a state
has a compelling interest in attaining a diverse student
body for the benefit of all students, and that this com-
pelling interest justifies the consideration of race as a
factor in university admissions. See 539 U.S. 306, 325,
328 (2003). In this, the latest case to consider the con-
stitutionality of affirmative action admissions policies,
Professor Richard H. Sander, along with lawyer and
journalist Stuart S. Taylor, Jr., filed a brief amici
curiae arguing that social science research has shown
affirmative action to be harmful to minority students.
See Brief Amici Curiae for Richard Sander and Stuart
Taylor, Jr. in Support of Neither Party (“Sander Fisher I
Brief”) at 2; see also Brief Amicus Curiae for Richard
Sander in Support of Neither Party (“Sander Fisher I]
Brief”) at 2. According to them, a “growing volume of
very careful research, some of it completely unrebut-
ted by dissenting work” has found that affirmative
action practices are not having their intended effect.
Sander Fisher I Brief at 2; see Brief Amici Curiae of
Gail Heriot et al. in Support of Petitioner (“Three
Commissioners Brief”) at 14 (“The Commissioner Amici
are aware of no empirical research that challenges

[Sander’s] findings.”).

Sander Fisher II Brief cites a handful of more recent articles, it
ignores contrary evidence, see, for example, Alice Xiang & Donald
B. Rubin, Assessing the Potential Impact of a Nationwide Class-
Based Affirmative Action System, 30 Stat. Sci. 297 (2015), and
relies in large part on the same “mismatch” research that amici
demonstrated in their brief amici curiae in Fisher I is fundamen-
tally flawed.

9

But, as amici will show, the principal research on
which Sander and Taylor rely for their conclusion about
the negative effects of affirmative action—Sander’s so-
called “mismatch” hypothesis*—is far from “unrebutted.”
Sander Fisher I Brief at 2. Since Sander first pub-
lished findings in support of a “mismatch” in 2004,
that research has been subjected to wide-ranging crit-
icism. Nor is Sander’s research “very careful.” Id.
As some of those critiques discuss in detail, Sander’s
research has major methodological flaws—misapplying
basic principles of causal inference—that call into doubt
his controversial conclusions about affirmative action.
The Sander “mismatch” research—and its provocative
claim that, on average, minority students admitted
through affirmative action would be better off attend-
ing less selective colleges and universities—is not good
social science.

Sander’s research has “significantly overestimated
the costs of affirmative action and failed to demon-
strate benefits from ending it.” David L. Chambers et
al., The Real Impact of Affirmative Action in American
Law Schools: An Empirical Critique of Richard Sander’s
Study, 57 Stan. L. Rev. 1855, 1857 (2005). That research,
which consists of weak empirical contentions that
fail to meet the basic tenets of rigorous social science
research, provides no basis for this Court to revisit

* In essence, “mismatch” is said to result when a minority stu-
dent attends a more selective university than he would have
without affirmative action, based upon a “very large” racial pref-
erence. The claim is that because the student's test scores and
high school or college grades indicate that he is not as academi-
cally qualified to attend the school at which he matriculates as
other students, his admission there works to his detriment because
“teachers would aim instruction at the median student, and those
with weaker preparation would fall behind and learn less.”
Sander Fisher I Brief at 4; see Sander Fisher I] Brief at 17-18.

10

longstanding precedent supporting the individualized
consideration of race in admissions. Cf. “Frutter, 539 U.S.
at 334 (“Universities can * * * consider race or ethnic-
ity more flexibly as a ‘plus’ factor in the context of
individualized consideration of each and every appli-
cant.”) (citing Regents of Univ. of Cal. v. Bakke, 438
U.S. 265, 315-316 (1978) (opinion of Powell, J.)). In
light of the significant methodological flaws on which
it rests, Sander’s research does not constitute credible
evidence that affirmative action practices are harmful
to minorities, let clone that the diversity rationale at
the heart of Grutter is at odds with social science.

ARGUMENT

This Court has held the use of narrowly tailored
race-based admissions constitutional in light of the
“substantial * * * educational benefits that flow from
student body diversity.” Grutter, 539 U.S. at 330. In
their brief amici curiae—nominally filed in support of
neither party (see generally Sander Fisher I Brief) but
nonetheless calling for the judgment of the Fifth Cir-
cuit to be reversed, id. at 36—Sander and Taylor argue
that “a growing volume of very careful research * * *
suggests that racial preferences in higher education
often undermine minority achievement,” id. at 2; see
Sander Fisher II Brief at 17; accord Three Commissioners
Brief at 13-14, 20-21, 24, which in turn purportedly
undermines the diversity holding in Grutter, see Sander
Fisher I Brief at 15-17. But that “growing volume of
very careful research” consists mostly of Sander’s own
“mismatch” research along with unpublished papers by
several others. As recognized by rumerous publications
refuting Sander’s conclusions, that research contains
basic methodological errors, thereby invalidating its
conclusions. As a result, its conclusions are without
value to the Court.

11
A. “Mismatch” Hypothesis In Brief

Although Sander was not the first researcher to use
the term “mismatch” in discussing the effects of race-
based admissions policies,‘ he has been rightly cred-
ited as a leading proponent of that theory since the
publication of his controversial Stanford Law Review
article finding “mismatch” in American law schools.
See Richard H. Sander, A Systemic Analysis of Affirm-
ative Action in American Law Schools, 57 Stan. L. Rev.
367 (2004). In that article and in later work, Sander
argues that when a minority student attends a college
or graduate school as a result of race-based admis-
sions, and his academic credentials are substantially
below those of his classmates, he does not thrive. For
example, as a result of being surrounded by students
with stronger academic credentials, a minority student
may end up in the bottom of the grade distribution in
difficult classes, and end up opting out of difficult majors
that he may have chosen otherwise (often called “science
mismatch”). Sander Fisher I Brief at 6; see Sander
Fisher II Brief at 18. Similarly, as a result of race-
based admissions, a minority student may choose not
to pursue graduate-school education (“academic mis-
match”), Sander Fisher I Brief at 7-8, Sander Fisher IJ
Brief at 17-20, or in the case of a law school student,
may fail the bar exam (“law school mismatch”), id. at 8;
see generally Sander, A Systemic Analysis of Affirmative
Action, supra. Ultimately, the mismatch hypothesis
holds that affirmative action does more harm than
good for minority students, whom Sander and Taylor
term “the intended beneficiaries” (Sander Fisher I

* See Thomas Sowell, Black Education: Myths and Tragedies
(David McKay 1972); Rogers Elliott et al., The Role of Ethnicity
in Choosing and Leaving Science in Highly Selective Institutions,
37 Res. Higher Educ. 681 (1996).

12

Brief at 2, Sander Fisher II Brief at 16) of affirmative
action. Hence, by curtailing affirmative action, minor-
ities would end up at schools more properly “matched”
to their skill sets, and minority academic performance
and graduation rates would rise. See Sander Fisher I
Brief at 32-35.

Mismatch research is premised on a series of causal
inferences. For example, the mismatch hypothesis is
that African-American undergraduates have transferred
out of rigorous science majors because of mismatch.
Id. at 6-7; see Sander Fisher I] Brief at 18, Richard
Sander & Roger Bolus, Do Credential Gaps in College
Reduce the Number of Minority Science Graduates?
(Project SEAPHE Working Paper, July 2009), available
at http://Awww.seaphe.org/working-papers/. Proponents
of this research claim that mismatch at law schools
has caused African-Americans to learn less in law school,
earn lower grades, fail the bar, and fare worse in
employment outcomes. Sander Fisher I Brief at 8-9,
see Sander Fisher II Brief at 17, 24. And, most contro-
versially, Professor Sander contends that affirmative
action has had the effect of decreasing the total num-
ber of black lawyers. Sander, A Systemic Analysis of
Affirmative Action, supra, at 473.

B. Sander’s “Mismatch” Research Does Not
Represent A Consensus In Social Science

Since the initial publication of his “mismatch” article,
Sander’s work has been subject to wide-ranging criti-
cism for its methodological flaws. See, e.g., lan Ayres
& Richard Brooks, Does Affirmative Action Reduce the
Number of Black Lawyers?, 57 Stan. L. Rev. 1807,
1809 (2005) (“[E]ven within his [Sander’s] framework,
there is not persuasive evidence indicating that affirma-
tive action is responsible for lowering the number of

13

black attorneys.”); Gregory Camilli et al., The Mismatch
Hypotheses in Law School Admissions, 2 Widener J.L.
Econ. & Race 165, 207 (2011) (“[T]his study has shown
that regression analyses of the kind conducted by
Sander are incapable of producing credible estimates
of causal effects.”); Chambers et al., supra, at 1857
(“The conclusions in Systemic Analysis rest on a series
of statistical errors, oversights, and implausible assump-
tions.”); Michele Landis Dauber, The Big Muddy, 57
Stan L. Rev. 1899, 1902 (2005) (“Unfortunately, Sander
has muddied rather than clarified the waters with
a flawed and ultimately misleading contribution.”);
Chery] I. Harris & William C. Kidder, The Black Student
Mismatch Myth in Legal Education: The Systemic
Flaws in Richard Sander’s Affirmative Action Study,
J. Blacks Higher Educ. (2005) (“Regrettably, Sander
significantly underestimates the harms of ending
affirmative action, and seriously overestimates the
benefits of ending affirmative action. Even his own data
do not support the mismatch hypothesis.”); Daniel E. Ho,
Why Affirmative Action Does Not Cause Black Students
to Fail the Bar, 114 Yale L.J. 1997, 1997 (2005) (“[T]he
[Sander] study draws internally inconsistent and empiri-
cally invalid conclusions about the effects of affirma-
tive action. Correcting the assumptions and testing
the hypothesis directly shows that for similarly quali-
fied black students, attending a higher-tier law school
has no detectable effect on bar passage rates.”); Daniel
E. Ho, Affirmative Action’s Affirmative Actions: A Reply
to Sander, 114 Yale L.J. 2011, 2011 (2005) (“[T]he
impressive-sounding points in Sander’s Response vio-
late basic methodological principles and are incorrect.”);
Beverly I. Moran, The Case for Black Inferiority? What
Must Be True If Professor Sander Is Right: A Response
to A Systemic Analysis of Affirmative Action in
American Law Schools, 5 Conn. Pub. Int. L.J. 41, 58

14

(2005) (“In the end, Professor Sander’s arguments fail
on their methodology, their logic, and their real-world
application.”); Angela Onwuachi-Willig & Amber Fricke,
Class, Classes, and Classic Race-Baiting: What's in a
Definition?, 88 Denv. U.L. Rev. 807, 834 (2011) (“[Wle
perceive numerous defects in Sander’s methodology
that raise serious questions about the results in his
article Class in American Legal Education.”).

The hallmark of reliable empirical work is that it
can be validated by other researchers. A wide array of
social scientists have studied the impact of elite educa-
tional institutions on student outcomes, reaching conclu-
sions directly contrary to those of mismatch. See, e.g.,
Sigal Alon & Marta Tienda, Assessing the “Mismatch”
Hypothesis: Differences in College Graduation Rates by
Institutional Selectivity, 78 Soc. Educ. 294, 309 (2005)
(“Minority students’ likelihood of graduation increases
as the selectivity of the institution attended rises.”);
Kalena E. Cortes, Do Bans on Affirmative Action Hurt
Minority Students? Evidence from the Texas 10% Plan,
29 Econ. Educ. Rev. 1110, 1122 (2010) (“[Rjesults from
the analysis run counter to the ‘mismatch’ hypothesis,
which would have predicted both higher retention and
college graduation rates for these lower-ranked minor-
ity students because they are now supposedly being
better ‘matched’ to an institution under the Top 10%
Plan.”); Mary J. Fischer & Douglas S. Massey, The
Effects of Affirmative Action in Higher Education, 36
Soc. Sci. Res. 531, 544 (2007) (“If anything[,] minority
students who benefited from affirmative action earned
higher grades and left school at lower rates than others,
and they expressed neither greater nor less satisfac-
tion with college life in general.”); Thomas J. Kane,
Racial and Ethnic Preferences in College Admissions,
59 Ohio St. L.J. 971, 991 (1998) (“[E]ven if a student’s
characteristics are held constant, attendance at a more

15
selective institution is associated with higher earnings
and higher college completion rates for minority students
as well as white and other non-Hispanic students.”);
Mark C. Long, College Quality and Early Adult Out-
comes, 27 Econ. Educ. Rev. 588, 589 (2008) (“[ClJollege
quality does appear to have positive significant effects
on most of the outcomes studied * * *.”); Tatiana
Melguizo, Quality Matters: Assessing the Impact of
Attending More Selective Institutions on College Com-
pletion Rates of Minorities, 49 Res. Higher Educ. 214,
232 (2008) (“{MJinorities benefit from attending the
most elite institutions.”); Tatiana Melguizo, Are Stu-
dents of Color More Likely to Graduate from College If
They Attend More Selective Institutions? Evidence from
a Cohort of Recipients and Nonrecipients of the Gates
Millennium Scholarship (GMS) Program, 32 Educ.
Eval. & Pol’y Analysis 230, 244 (2010) (“The results
of this study suggest that the probability of attaining
a bachelor’s degree increases [for minority students]
with the selectivity of the institution attended.”); Jesse
Rothstein & Albert Yoon, Affirmative Action in Law
School Admissions: What Do Racial Preferences Do?,
75 U. Chi. L. Rev. 649, 707 (2008) (“Even overstating
mismatch effects and understating the importance of
preferences to enrollment, the effects of eliminating
mismatch are dwarfed by the first-order effect of elim-
inating preferences: the reduction in the number of
black students admitted.”); Mario L. Small & Christopher
Winship, Black Students’ Graduation from Elite Colleges:
Institutional Characteristics and Between-Institution
Differences, 36 Soc. Sci. Res. 1257, 1257 (2007)
(“(SJelectivity improves black probabilities of gradua-
tion.”); Doug Williams, Does Affirmative Action Create
Educational Mismatches in Law Schools? 42 (Working
Paper, Apr. 2009), available at econ.duke.edu/~hfl4/
ERID/Williams.pdf (“All of the previous papers that

16

have conducted formal tests of the mismatch hypothe-
ses have concluded that there is no evidence of mis-
match effects in the [Bar Passage Study].”); see also
William G. Bowen & Derek Bok, The Shape of the
River 259 (Princeton Univ. Press 2000) (“[T]he more
selective the college attended, the lower the black
dropout rate.”); William G. Bowen et al., Crossing the
Finish Line: Completing College at America’s Public
Universities 210 (Princeton Univ. Press 2011) (“There
is certainly no evidence that black men were ‘harmed’
by going to the more selective universities that chose
to admit them. In fact, the evidence available strongly
suggests that students in general, including black stu-
dents, are generally well advised to enroll at the most
challenging university that will accept them.”).

In short, those relying on mismatch research mis-
characterize the state of social science evidence and
describe a consensus that does not exist. The Court
should not rely on the mismatch research.*

For clarity, amici will explain why the research on
which mismatch rests is dubious.

C. The “Mismatch” Research Violates Basic
Principles Of Causal Inference

The chief empirical research offered by the Sander
Fisher I Brief—little of which is peer-reviewed and
most of which remains unpublished—violates basic
principles of causal inference that are widely accepted
in the scientific community. As amici explain, there is
no evidence that affirmative action hurts minority

* Indeed, were this a district court proceeding, mismatch
research should not pass the core Daubert tests of surviving peer

review and being generally accepted by experts in the field. See
generally Daubert v. Merrill Dow Pharm., 509 U.S. 579 (1993).

17

students who attend school under such programs.®
Attending a more elite school does not appear to cause
those students harm.

l. The Evidence

Although mismatch has been discussed in a variety
of contexts, Sander posits that the law school setting
is “uniquely appropriate for studying the mismatch
effect,” because, unlike in other higher education set-
tings, the bar exam is “more or less [a] uniform test/]
taken by graduates to measure their legal learning.”
Sander Fisher I Brief at 8. Much of the cited research
has been in the law school context. Amici therefore focus
the rest of their arguments on the methodological flaws
contained in Sander’s and economics professor Doug
Williams’s law school mismatch research (which dom-
inate the empirical findings of the Sander Fisher I
Brief), although the same methodological challenges
also affect mismatch research in other settings.

Law school mismatch alleges that large racial pref-
erences have “seriously damaged the academic perfor-
mance of black law students, contributing to lower
graduation rates and much lower success rates on bar
exams.” Id. at 8 (citing Sander, A Systemic Analysis
of Affirmative Action, supra, at 440-448), see Sander
Fisher II Brief at 17, 24. Finding that a student’s law

* This proposition is counterintuitive because it would imply
that the very fact of giving minority students extra options, by
admitting them to more selective institutions, harms them, and
that these students would personally benefit from being prevented
from attending such institutions. It rests on the presumption
that these students are themselves not good judges of what is in
their interest, and that given the option of attending a more
selective institution they would fai] to make the “right” choice of
attending the leas selective institution.

18

school grades have a stronger association with bar
passage than the tier of the law school the student
attended, Sander concludes that less-qualified stu-
dents are better off attending a less-selective school
where they will perform better and thus be more likely
to pass the bar exam. Sander, A Systemic Analysis
of Affirmative Action, supra, at 426, 429. The central
empirical claim is that going to a higher-tier law school
causes less qualified students to learn less, as reflected
through lower grades, and decreases bar performance
by a greater factor than school quality increases it. See
id. at 449-450; Williams, supra, at 9.

2. Research Principles For Causal Inference

A causal effect is the difference between two “poten-
tial outcomes.” For example, a law student may have
one potential outcome of career trajectory if he attend-
ed a higher-tier law school and another potential out-
come of career trajectory if he attended a lower-tier
law school. The difference between these two potential
outcomes is the causal effect of law school tier on that
student. The “fundamental problem of causal infer-
ence” is that researchers never observe both potential
outcomes. See Paul W. Holland, Statistics and Causal
Inference, 81 J. Am. Stat. Ass’n 945, 947 (1986); Donald
B. Rubin, Bayesian Inference for Causal Effects: The
Role of Randomization, 6 Annals Stat. 34, 38 (1978).
Causal inference thereby always involves estimating
the counterfactual outcome with observed data, e.g.,
how the student at a higher-tier school would have
fared had he gone to a lower-tier school. An experi-
ment addresses this problem, by comparing students
who are similar in pre-existing characteristics (e.g.,
ability), but are randomly assigned to different tier
schools. Because the two experimental groups would
differ only in tier of school attended, differences in the

19

outcomes for the two groups would provide a valid
estimate of the causal effect of law school tier. See
generally Guido W. Imbens & Donald B. Rubin, Causal
Inference in Statistics and Social Sciences (Cambridge
Univ. Press 2015); Donald B. Rubin, For Objective
Causal Inference, Design Trumps Analysis, 2 Annals
Applied Stat. 808 (2008); Donald B. Rubin, The Design
Versus the Analysis of Observational Studies for Causal
Effects: Parallels with the Design of Randomized Trials,
26 Stat. Med. 20 (2007); Donald B. Rubin, Estimating
Causal Effects of Treatments in Randomized and Non-
randomized Studies, 66 J. Educ. Psychol. 688 (1974).

Although actually conducting such an experiment is
obviously infeasible, the experimental model highlights
the primary task of research with data in which stu-
dents have not been randomly assigned. Specifically,
to draw a causal inference, researchers should gener-
ate (a) comparison groups that are (b) as similar as
possible in pre-existing characteristics, so that (c) dif-
ferences in outcomes can be attributed to the selectiv-
ity of the institution. Imbens & Rubin, supra, at ch. 15.
In each of these three regards, the Sander/Williams
evidence falls short.

3. Methodological Flaws

The Sander empirical evidence consists of “regression
analysis” that predicts bar passage for all students
that graduated law school with the variables of under-
graduate GPA, LSAT score, gender, race, law school
tier, and law school GPA. See Sander, A Systemic
Analysis of Affirmative Action, supra, at 444-445. The
association between law school tier and bar passage is
positive, but smaller in absolute magnitude than the
association between law school GPA and bar passage.
Id. Based on this analysis, Sander concludes that less-

20

qualified students should attend lower-tier law schools
where they would presumably achieve higher grades
and that affirmative action hurts minority students.
Id. at 444 tbl.6.1.

That inference is invalid for three reasons.
a. Invalid Comparisons

As to the broad claim about the causal effect of
affirmative action, the current analyses are simply
uninformative. All the schools in the bar-passage data
employ some form of affirmative action. Because there
is no comparison group of schools that do not practice
affirmative action, no broad inference about the effects
of affirmative action can be sustained.’

Moreover, the primary comparison that Sander and
Williams employ is that of black and white students.
See Richard H. Sander, Mismeasuring the Mismatch:
A Response to Ho, 114 Yale L.J. 2005, 2006 (2005) (“The
entire [Sander Stanford Law Review] paper is organized
around a comparison of ‘treatment’ blacks * * * and
‘control’ whites * * *.”); Williams, supra, at 18 (“[Ojne
approach is to use black as a proxy for being negatively
mismatched and white as a proxy for being matched.”).
This comparison assumes that black students at selec-
tive institutions would have fared similarly to white
students at less-selective institutions in the absence
of affirmative action. For example, the estimate for
how a black student at Yale Law School would have
performed at a lower-tier school might be based on a
white student at the University of Alabama Law

’ The extent of preferential admissions may of course vary by
school, and capitalizing on these differences may provide one
approach to assess different types of implementations of affirma-
tive action programs.

21

School.* This comparison violates the principle of
creating groups that are comparable in all pre-existing
respects except for law school tier. Usual tenets of
research design require that a study hold constant pre-
existing attributes such as race and gender. See Imbens
& Rubin, supra, at ch. 12. By comparing black students
at higher-tiered schools with white students at lower-
tiered schools, Sander and Williams violate these basic
principles.

6. Adjusting For Pre-Existing Characteristics

Proper research design requires that we compare
students with similar pre-existing characteristics, who

* Using Yale Law School and the University of Alabama Law
School is consistent with the Sander and Williams coding of the
first two tiers of law schools, although no specific schools are ever
disclosed in the data. The LSAC Bar Passage Study clusters
anonymized schools based on factors such as cost, size, selectivity,
faculty/student ratio, percent minority, and average LSAT and
undergraduate GPA. See Linda F. Wightman, User’s Guide:
LSAC National Longitudinal Data File 8 (1999). Sander reorders
these clusters by the median entering credentials of white
students to create a tier system. Sander, A Systemic Analysis of
Affirmative Action, supra, at 416. Sander’s top tier includes 16
schools that “are the most selective and the most expensive” with
“the highest UGPAs and LSAT scores.” Wightmzn, supra, at 16;
Sander, A Systemic Analysis of Affirmative Action, supra, at 430.
Sander’s second tier includes “14 large, highly selective law
schools that enroll student bodies that have UGPAs and LSAT
scores that are among the highest in the country.” Wightman,
supra, at 16; Sander, A Systemic Analysis of Affirmative Action,
supra, at 430. Using the current U.S. News and World Report
rankings (not tiers), Yale would likely be a tier-one school and the
University of Alabama Law School would likely be a tier-two
school under Sander’s coding. See U.S. News and World Report,
Best Law Schools, http://grad-schools.usnews.rankingsandrevi
ews.com/best-graduate-schools/top-law-schools/law-rankings (last
visited October 26, 2015).

22

nonetheless attend different law school tiers. Sander
and Williams, however, adjust not only for pre-existing
characteristics, but also for outcomes. Specifically, their
bar passage analyses (i) hold constant law school grad-
uation (by examining only students that graduated),
see Sander, A Systemic Analysis of Affirmative Action,
supra, at 444; Williams, supra, at 14, and (ii) control
for law school grades, despite the fact that a central
component of the mismatch hypothesis is that law
school tier affects both law school graduativn and grades.
Not only is this approach inconsistent with mismatch,
but it will also fail to generate valid inferences about
the causal effect of law school tier. Adjusting for out-
comes will generally not result in valid estimates of
causal effects. See Andrew Gelman & Jennifer Hill,
Data Analysis Using Regression and Multilevel /
Hierarchical Models 188-190 (Cambridge Univ. Press,
2007); Imbens & Rubin, supra, at ch. 12; Daniel E. Ho
et al., Matching as Nonparametric Preprocessing for
Reducing Model Dependence ir. Parametric Causal
Inference, 15 Pol. Analysis 199, 202 (2007); Paul R.
Rosenbaum, The Consequences of Adjustment for a
Concomitant Variable That Has Been Affected by the
Treatment, 147 J. Royal Sta. Soc’y Series A (Gen.) 656
(1984).°

\

* Principled methods for addressing these issues exist. For
accounting for intermediate outcomes, see Constantine Frankgakis
& Donald B. Rubin, Principal Stratification in Causal Inference,
58 Biometrics 21 (2002). For accounting for law school students
that do not graduate, see Junni Zhang & Donald B. Rubin,
Estimation of Causal Effects via Principal Stratification When
Some Outcomes Are Truncated by “Death,” 28 J. Educ. & Behav.
Stat. 353 (2003); Donald B. Rubin, Causal Inference Through
Potential Outcomes and Principal Stratification: Applications to
Studies with “Censoring” Due to Death, 21 Stat. Sci. 299 (2006).

23

Suppose, for example, that we conducted the ideal
experiment, randomizing 200 students to attend selec-
tive and less-selective institutions. Assume that the
results are that 95 out of 100 graduate at the selective
institution, and that 70 pass the bar exam. Meanwhile,
at the less selective institution, 80 out of 100 graduate,
and 60 pass the bar. That experiment suggests that
students benefit from attending a selective institution,
both in terms of graduation and bar passage. If we
focus only on those who graduated, however, the bar
passage rate is 0.74 at the selective institution (70/95),
and 0.75 at the less seiective institution (60/80). Refer-
ring to these findings would lead a reader to wrongly
infer that a more selective law school harms students.
The selective law school, possibly via improved teach-
ing and better resources, manages to graduate more
students. But the subsets of students graduating from
either school are not fully comparable.

Adjusting for such outcomes (rather than pre-existing
characteristics), as the Sander studies do, contaminates
inferences about the causal effect of law school tier.
Indeed, controlling for graduation and grades leads
Sander to claim that there is no economic return to
attending an elite law school at all, regardless of
ethnicity. See Richard H. Sander & Jane Yakowitz
Bambauer, The Secret of My Success: How Status,
Prestige and School Performance Shape Legal Careers
(UCLA School of Law Research Paper No. 10-26, July
2010), available at http://papers.ssrn.com/sol3/papers.
cfm ?abstract_id=1640058 (finding that law school grades
are more highly correlated than Jaw school tier in a
regression of economic returns and that therefore elite
law schools have no causal effect on income).

24
c. The Effect Of Law School Tier

The credibility of a causal inference depends on the
credibility of the assumptions. One natural way for-
ward with the bar-passage data is to compare students
with identical observed pre-existing characteristics
(i.e., undergraduate GPA scores, LSAT scores, race,
and gender) who attend different law school tiers. The
critical assumption is that holding constant these fac-
tors, there are no other systematic differences between
students in the different law school tiers. In other words,
female black students with GPAs of 3.8 and LSAT
scores of 168, some of whom attend Yale Law School
and some of whom attend the University of Alabama
Law School, are comparable, and any systematic dif-
ferences in outcomes between those attending Yale
versus Alabama are due to the difference in the law
school attended. The existence of important unobserved
differences between these students that affect bar per-
formance invalidates the estimates. If the Yale Law
student, for example, is already predisposed to taking
the bar in a jurisdiction with a tougher exam such as
California or New York, the assumption is violated,
and the researcher would draw an inappropriate infer-
ence about the effect of law school tier.

Research that applies these principles has not found
any substantially and statistically significant effects
on bar passage.'’° See Ho, Why Affirmative Action Does
Not Cause Black Students to Fail the Bar, supra, at
2002-04. Taking into account these principles of research

‘0 Without proper research design, causal-effect estimates are
biased, and conventional tests for statistical significance (and
confidence intervals) do not address that bias. Put differently,
the fact that a result is “statistically significant” does not overcome
the first-order issues of research design that amici highlight.

25

design, there is simply no evidence of the harms of
mismatch suggested by the Sander Fisher I Brief.

D. Better-Designed Studies Contradict
“Mismatch”

It is possible to avoid the rather basic methodologi-
cal problems underlying the mismatch research. To
that end, amici draw this Court’s attention to exam-
ples of recent research that employ better-conceived
research designs with observational data.

Stacy Berg Dale and Alan B. Krueger have employed
careful methodology in two papers examining the
return from college selectivity over a student’s subse-
quent career. The first study was published a decade ago,
Estimating the Payoff to Attending a More Selective
College: An Application of Selection on Observables
and Unobservables, 117 Q.J. Econ. 1491 (2002). In 2011,
Dale and Krueger returned to the topic in Estimating
the Return to College Selectivity over the Career Using
Administrative Earnings Data (Nat’] Bureau of Econ.
Research, Working Paper No. 17159, June 2011),
available at http://www.nber.org/papers/w 17159.

In the recent work, they utilize tax data to examine
the earnings of students who attended college in both
1976 and 1989. The Dale and Krueger model considers
the college the student attended, conventional charac-
teristics, a plausible measure of what is typically un-
observed (ability and motivation), and the monetary
payoff of attending a more selective college against the
student’s actual earnings. See Dale & Krueger 2011,
supra, at 5-8.

In both studies, Dale and Krueger examine charac-
teristics that are commonly used as proxies for college
quality (average SAT score, the Barron’s index, and

26

net tuition). They also adjust for certain “unobservable
factors” by using a “self-revelation model.” This model
assumes that students signal their potential ability,
motivation, and ambition (typically unobserved) by
the choice of schools to which they apply. Dale and
Krueger estimate, somewhat surprisingly, that better
colleges generally do not increase earnings for either
the 1976 and 1989 cohort of students. Jd. at 25. They
find, however, “[n]otable exceptions * * * for racial and
ethnic minorities (black and Hispanic students) and
for students whose parents have relatively little edu-
cation; for these subgroups, our estimates remain large,
even in models that adjust for [typically] unobserved
student characteristics.” Jd. at 5. In other words,
the Dale and Krueger research suggests—contrary to
mismatch—that attending more selective colleges may
result in an increase in minority earnings.

They conclude their paper by mentioning the caveats
of their work, most notably that their analysis does not
pertain to a nationally representative sample of schools
and that their selection-adjusted model is imprecisely
estimated. Jd. at 25. Nowhere do Dale and Krueger
control for outcomes, and their analysis holds constant
minority status. Unlike Sander’s mismatch research,
Dale and Krueger exercise appropriate caution in making
far-ranging conclusions from a limited set of data.

*_*

Whether one finds Sander’s conclusions highly un-
likely or intuitively appealing, his “mismatch” research
fails to satisfy the basic standards of good empirical
social science research. The Sander Fisher I Brief mis-
represents the acceptance of his hypothesis in the
social science community and, ultimately, the validity
of mismatch. Numerous examples exist of better ways
to perform the type of research Sander undertook. See

27

Part D, supra. Sander’s failure to set up proper controls
to test his hypothesis and his reliance on a number of
contradictory assumptions lead him to draw unwar-
ranted causal inferences. At a minimum, these basic

research flaws call into question the conclusions of
that research.

CONCLUSION

In light of the many methodological problems with
the underlying research, amici curiae respectfully
request that the Court reject Sander’s “mismatch”
research discussed in the Brief Amici Curiae for
Richard Sander and Stuart Taylor, Jr.

Respectfully submitted, ''

IVAN ESPINOZA-MADRIGAL A.W. PHINNEY III
OREN M. SELLSTROM Counsel of Record
MATTHEW CREGOR YALONDA T. HOWZE
LAWYERS’ COMMITTEE COLIN G. VAN DYKE

FOR CIVIL RIGHTS AND MATHILDA S. MCGEE-TUBB

ECONOMIC JUSTICE MINTZ, LEVIN, COHN, FERRIS,
294 Washington Street GLOVSKY AND POPEO, P.C.
Suite 443 One Financial Center
Boston, MA 02108 Boston, MA 02111
(617) 482-1145 (617) 542-6000

AWPhinney@Mintz.com

November 2, 2015

'\ Amici acknowledge and thank the authors of the original
brief amici curiae filed in Fisher I: Harry M. Reasoner, Thomas
S. Leatherbury, Kimberly R. McCoy, and Eric A. White, from the
law firm of Vinson & Elkins LLP.

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Source: Frix Law Library, https://www.frixlaw.com/law-library/documents/brief%3Amicro_IA40385016_0328%3A051. Public record. Not legal advice.