Transcript Document

Fisher v. Texas: The Limits of Exhaustion and
the Future of Race-Conscious University Admissions
Professor john a. powell
Director, Haas Institute for a Fair and Inclusive Society;
The Robert D. Haas Chancellor’s Chair in Equity and Inclusion
February 22, 2014
Fisher v. Texas
7-1 Decision
Fisher on its face did not:
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strike down UT’s holistic admissions policy
overrule Grutter
revise or otherwise alter the constitutional standards announced in Grutter
hold that UT’s admissions policy was not narrowly tailored
suggest deficiencies in the UT policy
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"New Departures"
Three ways in which Fisher departs from precedent
Yet, upon a closer reading, Fisher is a departure from settled law in a number of critical
respects.
1. For the first time, 7Justices hold that the use of racial classifications – regardless of
intent – in university admissions should be subject to strict scrutiny review
2. Narrow tailoring now requires “exhaustion” of race neutral alternatives instead of
“consideration” of them.
3. “Good faith” consideration does not suffice. The court, not the University, is not
responsible for assessing the availability of alternatives.
Exhaustion Requirement
• Grutter held that narrow tailoring
– requires “serious, good faith consideration of workable race-neutral
alternatives.”
– “does not require exhaustion of every conceivable race-neutral alternative.”
• To the contrary, Justice Kennedy’s Fisher opinion asserts
– “the reviewing court must ultimately be satisfied that no workable race-neutral
alternatives would produce the educational benefits of diversity.”
Unanswered Questions
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What facts must be presented to satisfy a court that no workable race-neutral
alternatives are viable?
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What degree of certainty is called for in order to satisfy the reviewing court?
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How are we to understand the standard of ‘tolerable administrative expense’?
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Where is the threshold for tolerable expense or the line between tolerable and
intolerable expense?
The sociological complexity of race
illustrates the limits of exhaustion.
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As a social construction, race is not an essential or static characteristic, but a
dynamic one.
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It is the interaction of domains such as housing, education, employment, and
health, to take but a few, on each other that explains racialized outcomes. The
attempt to explain or measure the effects of racial discrimination in any particular
domain will be necessarily incomplete.
Gunnar Myrdal:
“The unity is largely the result of cumulative causation binding them all
together in a system and tying them to white discrimination. It is useful,
therefore, to interpret all the separate factors from a central vantage point – the
point of view of the Negro problem…In an interdependent system of dynamic
causation there is no ‘primary cause’ but everything is cause to everything
else.”
Racialized Populations
and Outcomes
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The relative disadvantage of certain racialized populations results from
dozens of demographic, social, and economic factors that may vary
across geographic areas and local conditions.
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The convergence of these factors with race makes race a particularly
useful consideration in understanding life chances, but it also makes it
vexing to analyze the various complex factors that explain race.
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An admissions policy limited to race-neutral factors cannot easily capture
their cumulative effect on educational opportunity.
Administrative Expense Caveat
“If a nonracial approach ... could promote the substantial interest
about as well and at tolerable administrative expense, then the
university may not consider race.”
The problem?
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The administrative expense of developing race-neutral plans goes far beyond
the resources of most admissions committees, let alone school boards and
administrative staff, compared to the use of racial classifications in either
student assignment or admissions review.
Complexity of Disadvantage
• Multi-dimensional/multi-indicator approaches are the
future.
• A single indicator cannot capture the myriad factors that
influence an individual’s life chances.
• Multi-factor approaches are compelling because they not
only paint a more vivid portrait of the underlying structural
conditions, but are also narrowly tailored particular forms
of disadvantage.
Alternative: Opportunity Enrollment
• Opportunity scoring is a sophisticated multi-factor methodology
that better captures disadvantages than a single indicator.
• Opportunity scoring creates an index of factors which correlate
to and causally explain life outcomes and projected life chances.
• The opportunity mapping methodology seeks to understand the
distribution of opportunity over space. Given this geographic
dimension, these indices can be represented using geographic
information technology in the form of opportunity maps. (See
http://egis.hud.gov/affht_pt/)
Opportunity Enrollment Model cont.
• Universities can use opportunity index scoring to target the
most educationally disadvantaged students and generate racial
and other forms of diversity.
• Applicants can be given an opportunity score based on a
mixture of both individual and geographic characteristics.
• For example, given an index of a particular region, universities
could set a hard quota that 20% of their enrollees are accepted
from low opportunity census tracts.
• Or, students who were raised or currently reside in neighborhoods
in low or very low opportunity census areas could also be
awarded a mechanical bonus in the admissions process.
Opportunity Enrollment Model cont.
• Neither approach would violate the Court's prohibition against
racial quotas or mechanical use of race, because such bonuses
are based on geographic residence, not race.
• Opportunity enrollment employs a mixture of geographic
diversity and socio-economic diversity.
• Because the vast majority of families residing in low or very low
opportunity census areas are African-American, this would have
a positive effect on racial diversity.
• In addition, the intense hyper-segregation of Black and Latino
families increases the probability that a geographic diversity plan
would work.
Multi-Indicator Approaches
EDUCATION
• Student poverty rates
• Reading/Math test scores
• Adult educational
attainment
• Teacher qualifications
• Graduation rate
ECONOMIC HEALTH
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Proximity to employment
Commute times
Job growth trends
Business start trends
Unemployment rate
Public assistance rate
HOUSING & NEIGHBORHOOD HEALTH
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Home ownership rates
Crime incidence
Vacancy rates
Home value appreciation
Neighborhood poverty rates
Population change
Proximity to parks/open space
Proximity to toxic waste release
sites
HUD’s Affirmatively
Fair Housing App
http://egis.hud.gov/affht_pt/
Next:
Stephen Menendian will cover post-Fisher
alternatives and specific examples where Opportunity
Enrollment Models and Mapping have been used.
Fisher v. Texas: Implications for K-12 Integration
Stephen Menendian
Assistant Director, Haas Institute for a Fair and
Inclusive Society
February 22, 2014
K-12/Post-Fisher Environment
A complex landscape:
• Increased racial polarization
• Justice Kennedy’s concern for white resentment
• Increased racial and economic inequality
• Varying commitments to integration
Criticism of 10% Plan
• Less qualified students are admitted
• Relies on underlying patterns of segregation, which we should be
working to integrate
• Abandons racial diversity as an explicit goal as we pursue other
forms of diversity.
Opportunity Mapping
Since the racialized nature
of
Opportunity
Mapping
and
opportunity isolation is a spatial
phenomena, maps are naturally
Education
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an effective way to represent it
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Maps allow us to understand
volumes of data at a glance
through layering
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Mapping is a very powerful tool
in looking at educational
inequity & opportunity
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Opportunity Mapping For Schools
► Mapping the geographic distribution of
opportunity helps us to evaluate where
these opportunity mismatches exist in a
community and to design interventions to
move people to opportunity
► Student assignment policies can be
created using indicators, drawing
attendance Zones, boundaries, or through
controlled choice plans.
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District
Indicators
Steps
Notes
Jefferson
County/Louisvill
e, KY
1)
2)
Median HH Income
Racial Composition
of Neighborhood
Ed. Attain of Parents
1) Parental Choice
within Resides
Zone
Two-Zone model
Berkeley , CAL
1)
Average Nbhd
Income
Ed. Attain of Adults
in Nbhd
Racial Composition
of Nbhd
1)
2)
Sibling
Parental Choice
within Zone
assignment
Controlled Choice, 3
Attendance Zones;
Upheld by Cal. Ct. of
Appeals
Median HH income
HH Poverty Rates
# of F/R Lunch Stds
Ed. Attain of Adults
in Nbhd
Racial Composition
of Nbhd
1)
2)
3)
4)
Special needs
ESL
Siblings
Parental Choice
within Zone
Assignment
Magnets Plan,
Freedom-of-Choice,
3-Zones, K students
only
1)
2)
Siblings
½ of remaining seats
proximity lottery
Remaining Seats by
SES census block
zone
4 Census Block
Zones
3)
2)
3)
Montclair, NJ
1)
2)
3)
4)
5)
Chicago, IL
1)
2)
3)
4)
Median family income
Adult Ed. Attainment
% of Single-Parent HH
% of Owner-Occupied
Homes
5) % Of ESL students
3)
Berkeley Zones
Source: Civil Rights Project at UCLA
Diversity Map
Source: Civil Rights Project at UCLA
Cal. Ct. of Appeals
“We conclude that the particular policies challenged here – which aims to
achieve social diversity by using neighborhood demographics when
assigning students to schools – is not discriminatory. The challenged
policy does not use racial classifications; in fact, it does not consider an
individual student’s race at all when assigning the student to a school.”
- ACRF v. Berkeley Unified School Districts
Opportunity Zones in Montclair
Modeled several educational zones for Montclair, based on five equally
weighted factors.
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# of Free and Reduced Lunch Students
Parental Education Levels
Median Household Income
Household Poverty Rates
Race, by neighborhood
Each of these factors was calculated at the neighborhood level, by census
block group.
Montclair
*Step 3: From this database, a wait list system is utilized
Montclair
Montclair
Three Zone Integration Model: Montclair, NJ
Under the plan, the township would be divided into three zones,
labeled Zone A, Zone B and Zone C.
Students would be assigned to zones based on individual census
data, including household income and Title 1 status (eligibility for
Free or Reduced Lunch).
Students from all three zones would then be represented in each
school.
Three Zone Integration Model: Montclair,
NJ
►GOAL: Each school has
diversity of students from
each zone, within 5% point
deviation of K class zone
baseline.
►K and transfer students
are assigned based on
parental preference and
zone balance.
Three Opportunity
Zone Model
Without Race
With Race
Four Opportunity
Zone Model
Without Race
With Race
Why race still matters
• Alternatives lead to greater complexity, which places a burden on
school districts
• Empirical evidence is so far mixed on the success of these plans
• Multi-factor approaches may better capture particular forms of
disadvantage, but they do a less effective job of producing raw
numerical racial diversity than individual racial classifications do
• Alternative factors, including socioeconomic status, are imprecise
• Approximating race is resource intensive and requires outside
expertise and consultants
Justice Kennedy’s opinion is controlling as the fifth vote.
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That the school districts consider these plans to
be necessary should remind us that our highest
aspirations are yet unfulfilled. School districts
can seek to reach Brown’s objective of equal
educational opportunity. But the solutions
mandated by these school districts must
themselves be lawful.
“If school authorities are concerned that the studentbody compositions of certain schools interfere with the
objective of offering an equal educational opportunity
to all of their students, they are free to devise raceconscious measures to address the problem in a
general way without treating each student in a different
fashion soley on the basis of systematic, individual
typing by race.
School boards may pursue the goal of bringing together students of
diverse backgrounds and races through other means, including strategic
site selection of new schools; drawing attendance zones with general
recognition of the demographics of the neighborhoods; allocating
resources for special programs; recruiting students and faculty in a
targeted fashion; and tracking enrollments, performance, and other
statistics by race. These mechanisms are race-conscious but do not lead
to different treatment based on a classifications that tells each student he
or she is to be37defined by race.
Conclusion
• Opportunity-enrollment model may well offer an ideal alternative
or complementary admissions policy.
• Pursuit of policies such as these will illustrate for the courts the
limits of a strict exhaustion requirement, and perhaps lead to
the development and use of admissions processes that can
better measure forms of advantage relative to discrete and
insular minorities.