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

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.