hackdemocracy brussels 3: using technology to improve school choice procedures
DESCRIPTION
Presentation by Estelle Cantillon, "Using technology to improve School Choice Procedures"TRANSCRIPT
School Choice Procedures
Estelle Can3llon February 23, 2011
Why do we care? (alterna3ve being “laisser-‐faire”)
• Equity – In presence of scarcity – In urban contexts where students mobility is high and fashion/herding can create conges3on
• Private versus social preferences over school composi3on – Externali3es: social cohesion, academic diversity, .
Top-‐down or BoQom-‐up?
• Premise: We want to take parents / students’ preferences as much as possible into account – Some combina3on of top-‐down and boQom-‐up – Different countries / districts locate themselves differently on this scale
Need to be able to handle preference informa3on together with (poli3cal / school) priori3es
Three criteria for candidate procedures
• Efficiency – A procedure is efficient is there does not exist another alloca3on of students to schools such that every student is beQer off and at least one is strictly beQer off
• No jus1fied envy
– There is no student that has a place in a school, whereas another one who actually has priority over that student at that school, and prefers that school to the school he’s assigned to, does not have one.
• Strategic simplicity – It should be in the interest of parents to reveal their true preferences instead of manipula3ng them
– Equity and efficiency considera3ons
School choice mechanisms
• Inputs: – Reports by students over schools (rank order list, ROL)
– Quotas and student priori3es at each school – School capaci3es
• No procedure sa3sfy all three criteria when priori3es are not strict at all schools – Top trading Cycles and Deferred Acceptance best in class
Student-‐proposing deferred acceptance algorithm (Gale-‐Shapley) • Students submit their ROLs and schools their priori3es over
students (use of a 3e-‐breaker if necessary) • Step 1: Each student proposes to her first choice. Each
school tenta3vely assigns its seats to its proposers one at a 3me following their priority order. Any remaining proposer is rejected.
• … • Step k: Each student who was rejected in the previous step
proposes to her next choice. Each school considers the students it had tenta3vely accepted in the previous period together with the new proposers and accepts tenta3vely those with the highest priori3es. It rejects other.
• The algorithm terminates when no more requests are rejected.
Example
– 4 kids, 2 schools with 2 seats each – Student preferences:
– Student a: 1 2 – Student b: 1 2 – Student c: 1 2 – Student d: 2 1
– Priori3es over students: – School 1: a d b c – School 2: b a c d
Student-‐proposing DAA, first round: Students apply to their first choice school. School 1 rejects student c
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Round 2: Student c applies to school 2 and is accepted
Comments
• Centraliza3on is necessary to make this run smoothly (takes a few minutes to run on a computer)
• Poli3cal objec3ves are translated into priori3es and quotas
• Interface for parents to input preferences
Ac3ve field of policy
• Many school districts are revamping their school choice procedures – Drivers: technology and pressure to introduce choice
• Not a “one-‐size-‐fits-‐all” solu3on – Tailoring to policy objec3ves needed – Parents’ aspira3ons and poli3cal acceptability
Ac3ve field of research
• Proper3es of procedures • Applica3ons and access to data open an opportunity to answer new ques3ons – Long term effects of school choice regula3on on school composi3on and student outcomes?
– Preference forma3on? • “Matching in Prac3ce” network gathers informa3on on procedures and outcomes across Europe
PRELIMINARY EVIDENCE FROM DUTCH-‐SPEAKING PRESCHOOLS IN BRUSSELS
Data
• Preschool popula3on in Dutch-‐speaking preschools in Brussels as of 1 October 2008 (10,867 kids, 150 schools, entering class 4079)
• Kid characteris3cs: age, loca3on, na3onality, GOK status, socioeconomic class of neighborhood, whether Dutch is spoken at home, school aQended
• School characteris3cs: loca3on, network, confessional orienta3on, establishments, pedagogy
Legal constraints on the mechanisms
Current procedure: – Siblings have priori3es over other kids – 30% quota for GOK students – 45% quota for Dutch na3ve speakers – Priori3es and quotas implemented through early registra3on periods
– First come, first served as a 3e-‐breaker – Decentralized
New GOK decree allows them to experiment with distance as a different 3e-‐breaker
Analysis of the current situa3on – heterogeneity across schools
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10% lowest 2 3 4 5 6 7 8 9 10% highest
Percentage of GOK students and na1ve speakers across schools
% GOK students
% Dutch @ home
Analysis of the current situa3on – distance to school
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0.05
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0.15
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0.25
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0.35
closest 2 to 3 4 to 5 6 to 10 11 to 15 16 to 20 21 to 30 above 30
Brussels kids going to preschool in Brussels -‐ closest school
whole sample
low socio
high socio
gok
Dutch @ home
1141 incoming students, 958 outgoing students,
Genera3ng a counterfactual policy experiment • LOP Brussels is considering to replace its 3me priority with a distance-‐based 3e breaker .
• How will kids be impacted? How will schools be impacted?
• Main challenge : We do not observe preferences over schools
Calibra3ng preferences Working assump3ons: – Current procedure can be approximated by a student-‐proposing DAA with socioeconomic status, then distance as a 3e-‐breaker
– Brussels-‐based students have preferences over Brussels schools that depend on their socioeconomic status (top 30%, GOK, other) uis = α1k distanceis + α2k qualitys + (1-‐ α1k -‐ α2k )εis They also have an outside op3on (random u3lity) and place the school where they have a sibling first
– Out-‐of-‐Brussels students have preferences that take the form uis = δ qualitys + (1-‐ δ)εis
Calibra3ng preferences (con3nued)
Calibrate these preferences so that predicted outcome (distribu3on of ranks of assigned school) close to actual outcome α1high = 0.55 α1GOK = 0.70 α1rest = 0.58 δ = 0.75
Weight on ε set to 0.05
Counterfactual 1: From 3me to distance as a 3e-‐breaker – aggregate results
Counterfactual 1: Distribu3onal aspects
Counterfactual 1: Impact on school popula3on
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10% lowest
2 3 4 5 6 7 8 9 10 % highest
Propor1on of Dutch na1ve speakers -‐ before and aQer
simulated "before" "ater" actual
Impact on school popula3on (cont’d)
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10% lowest
2 3 4 5 6 7 8 9 10 % highest
Propor1on of GOK students before and aQer, per decile of schools
simulated "before" "ater" actual
Counterfactual 1: Likely long term residen3al effects • Mean median distance to school goes from 1.45 km to 0.9 km
• Mean max distance to school goes from 11.17 km to 10.54 km – max distance goes down in 41 schools out of 147 – Min max distance goes from 0.94 km to 0.45 km
Counterfactual 2: School-‐proposing DAA
Illustra3on: écoles gardiennes NL de Bruxelles 1. Effet d’un quota sur la mixité sociale
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1er décile 2 3 4 5 6 7 8 9 10e décile
Simula3ons avec quota GOK Simula3ons sans quota GOK Situa3on actuelle
Propor3on d’élèves GOK par décile d’écoles
Illustra3on: écoles gardiennes NL de Bruxelles 2. Conséquences redistribu3ves
Illustra3on: écoles gardiennes NL de Bruxelles 3. De l’importance de la procédure
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1 11 21 31 41 51 61 71
premier arrivé, premier servi AAD-‐élèves AAD-‐écoles