Matt Kasman Doctoral Candidate Graduate School of Education May 19th, 2014

cepa.stanford.eduCENTER FOR EDUCATION POLICY ANALYSIS at STANFORD UNIVERSITY

School Selection, Student Assignment, and Enrollment in a School District with Open Enrollment and Mandatory Choice

Policies

Matt KasmanDoctoral Candidate

Graduate School of Education

May 19th, 2014

[email protected]

mailto:[email protected]


Motivation• Positive effects of racially diverse

educational environment (Orfield et al., 2008)– Tolerance for different perspectives– Reduces prejudice– Stronger cooperative and critical-thinking

skills• Benefits for individuals as well as

communities and society


Motivation• School choice often discussed as a tool to

improve diversity within schools– Provides families with the opportunity to

make choices outside of a framework of traditional neighborhood schools

• Predominantly single-race schools persist in open enrollment school districts– Under what, if any, policy conditions can

open enrollment produce better results?


Dissertation Outline• 3 Papers that examine the open enrollment

process:1. School selection: when presented with school

options, what choices do families make?2. School enrollment: when assigned to schools,

how do families respond?3. Agent-based model simulations: given behaviors

in previous two papers, how do policy interventions affect diversity?


Data• School application, assignment and enrollment data

from a highly diverse, large urban school district– Focus on families choosing Kindergarten programs– Focus on families without observable siblings in schools

• Student data• School program data


Simulations of Open Enrollment• School choice processes well suited for

simulation– Individual decisions lead to system-wide change– Intention is for schools and districts to respond to

changes in demand over time• Agent-based modeling is a good approach

– Heterogeneity in schools and students– Dynamic process– Potential exit of students and schools– Equilibrium state is not focus of simulation


1. Initialization: “plausible” cohort of prospective Kindergarten students sampled from actual 2009-2010 through 2012-2013 student data

2. School selection: race-specific participation probabilities; “choosers” submit ranked school selections that are based on findings from Paper 1

3. Student assignment: simplified version of deferred acceptance algorithm used by district

4. Student enrollment: student enrollment based on findings from Paper 2

5. Iteration: schools update characteristics based on enrollment, new cohort of students generated

6. Output: after 10 years, model stops and output by year is saved out

The Agent-based Model


School Selection (Paper 1)• Conditional logit models predicting probability of a

school program being selected as a family’s first choice:

• Basic models include school/program characteristics• Additional models include interactions with student

attributes


Predicting probability of school program selection with program characteristics and race interaction terms

Interaction TermsVARIABLES Base Coefficients Student is

BlackStudent is Hispanic

Student is Asian

Distance to School 0.138*** 1.909*** 1.111 1.029Distance to School (squared) 1.192*** 0.935*** 0.996 1.009Family in School Attendance Area 2.989*** 0.651** 0.736** 0.810+School Achievement (standardized) 2.421*** 0.472*** 0.901 0.718***School Enrollment (log) 11.15*** 0.782 0.441*** 3.364***Language Immersion Program 0.937 0.589*** 2.677*** 1.428***Bilingual Program 3.143*** 1.521 0.926 1.313School % Black 0.980*** 1.041*** 0.991 1.015+School % Hispanic 0.982*** 1.009 1.023*** 1.002School % Asian 0.968*** 1.020*** 1.002 1.043***School % FRPL (lagged) 0.991** 1.000 1.015*** 0.998Observations (# students) 494,525 (5,961)

Coefficients are odds ratios; *** p<0.001, ** p<0.01, * p<0.05, + p<0.1


Achievement levels of options by distance

-1.50

-1.25

-1.00

-0.75

-0.50

-0.25

0.00

0.25

0.50

0.75

1.00

1.25

1.50

Sch

ool A

chie

vem

ent (

stan

dard

ized

)

0.0 1.0 2.0 3.0 4.0 5.0 6.0Distance (Miles)

White Black Asian Hispanic

by Distance to School OptionAverage School Achievement


Student Assignment• Deferred acceptance student assignment

– “Strategy-proof”• Hierarchical priority:

1. Low test-score zone residence2. School attendance zone residence3. Random lottery number

• Unassigned student placement:1. Attendance zone General Education program2. Closest available General Education program


Enrollment (Paper 2)• Logit models predicting exit from public schools

subsequent to assignment:

• Models include family characteristics and assignment characteristics (either alone or relative to first choice characteristics)

• Also, similar multinomial logit models predicting exit or reassignment:


Predicting probability of enrollment response with assignment relative to first choice

Coefficients are odds ratios; *** p<0.001, ** p<0.01, * p<0.05, + p<0.1

(1) (2)VARIABLES reassigned exited reassigned exited

Black 1.085 0.441*** 1.542*** 0.450***Hispanic 1.228** 0.559*** 1.496*** 0.553***

Asian 0.773*** 0.363*** 0.736*** 0.343***School % Black 1.050*** 1.030***

School % Hispanic 1.028*** 1.018***School % Asian 1.027*** 1.019***

School % Same Race 0.978*** 0.990***

School Enroll (log) 0.210*** 0.250*** 0.182*** 0.300***School % FRPL 1.011*** 1.014*** 1.027*** 1.026***

Distance to School 1.219*** 1.078** 1.239*** 1.083**School Achievement (Standardized) 0.554*** 0.623*** 0.490*** 0.597***

Same Program Type 0.445*** 0.836+ 0.475*** 0.900Constant 0.241*** 0.185*** 0.205*** 0.179***

Observations 11,878 11,878 11,188 11,188


Simulations (Paper 3)• Simulated scenarios

– Baseline model: similar to current district policies– Full participation: upper bound on effect of interventions

that increase engagement– Full participation, Black and Hispanic students– Better information: replace achievement levels with value-

added as observed measure of quality– “simple” value-added– “more sophisticated” value-added

– Increase capacity: investment in growing popular schools/programs

– Change student assignment: similar to Dur et al. (2013); remove low test-score zone priorities


Results• Checking validity of simulations

– Compare simulated 2011-2012 cohort to actual cohort

• Enrollment trends in baseline simulation• Compare end-state school compositions across

simulated scenarios


-3

-2

-1

0

1

2

Sta

nd

ard

ized

Ach

ieve

ment (M

ath

and

EL

A)

Black Hispanic Asian White

Simulated Cohort

-3

-2

-1

0

1

2

Sta

nd

ard

ized

Ach

ieve

ment (M

ath

and

EL

A)


Actual Cohort

assignments based on choices submitted by application deadline

by race, for Fall 2011 Kindergarten cohortAverage Achievement in Assigned School


0

20

40

60

80

100

% S

am

e R

ace


Simulated Cohort

0

20

40

60

80

100

% S

am

e R

ace


Actual Cohort

assignments based on choices submitted by application deadline

by race, for Fall 2011 Kindergarten cohortPercentage of Same Race Students in Assigned School


0

5

10

15

20

25

% b

y ra

ce

Black Hispanic Asian WhiteOverall attrition: 12.2%

Simulated Cohort

0

5

10

15

20

25

% b

y ra

ce

Black Hispanic Asian WhiteOverall attrition: 11.9%

Actual Cohort

students without siblings

by race, for Fall 2011 Kindergarten cohortAttrition from District


-1

-.5

0

.5

1

Sta

nda

rdiz

ed A

chie

vem

ent

(Mat

h a

nd

ELA

)

1 2 3 4 5 6 7 8 9 10Year


by race and year, baseline simulationAverage Achievement in Enrolled School


0

20

40

60

80

% S

ame

Ra

ce

1 2 3 4 5 6 7 8 9 10Year


by race and year, baseline simulationMean % Same Race in Enrolled School


1.00

0.97

1.04

0.99

0.850.87

1.08

.6

.8

1

1.2

Ga

p S

ize

(Sta

nda

rdiz

ed S

cho

ol A

chie

vem

ent L

evel

s)

BaselineSimulation

FullParticipation

Full Blackand HispanicParticipation

Double Capacityin HighestDemand

Programs

SimpleValue-added

More ComplexValue-added

No Priority forLow Test ScoreZone Residents

year 10 of simulation

between Black/Hispanic and White/Asian students, by simulationGap in Average Achievement in Enrolled Schools


22

10

19

8

22

9

24

8

19

12

16

14

23

11

H=.241

H=.226

H=.235 H=.242 H=.235H=.241

H=.255

0

10

20

30

40#

Rac

ially

Isol

ated

Sch

ools

BaselineSimulation

FullParticipation



Programs

SimpleValue-added




across simulated scenariosComparing Racial Isolation

# Schools > 60% and < 80% # Schools > 80%

22

10

19

8

22

9

24

8

19

12

16

14

23

11

H=.241

H=.226

H=.235 H=.242 H=.235H=.241

H=.255

0

10

20

30

40

# R

acia

lly Is

olat

ed S

choo

ls

BaselineSimulation

FullParticipation



Programs

SimpleValue-added




across simulated scenariosComparing Racial Isolation

# Schools > 60% and < 80% # Schools > 80%


Discussion• Stability in enrollment trends in baseline

simulation– Realistic, given longstanding choice policies in district

• Simulated interventions have effect on enrollment patterns

– Value-added information reduces racial gaps in enrolled school achievement levels

– More participation in choice increases diversity within schools

• Simulations of open enrollment are a tool that can be used to answer many questions


School Selection, Student Assignment, and Enrollment in a School District with Open Enrollment and Mandatory Choice

Policies

Matt KasmanDoctoral Candidate

Graduate School of Education

May 19th, 2014

[email protected]

mailto:[email protected]

Matt Kasman Doctoral Candidate Graduate School of Education May 19th, 2014

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