HackDemocracy Brussels 3: Using technology to improve School Choice Procedures
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HackDemocracy Brussels 3: Using technology to improve School Choice Procedures

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Presentation by Estelle Cantillon, "Using technology to improve School Choice Procedures"

Presentation by Estelle Cantillon, "Using technology to improve School Choice Procedures"

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HackDemocracy Brussels 3: Using technology to improve School Choice Procedures HackDemocracy Brussels 3: Using technology to improve School Choice Procedures Presentation 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-­‐proposing  DAA,  first  round:     –  Student  b:      1      2   Students  apply  to  their  first  choice   –  Student  c:      1      2   school.  School  1  rejects  student  c     Round  2:  Student  c  applies  to  school   –  Student  d:      2      1   2  and  is  accepted  –  Priori3es  over  students:       –  School  1:    a      d      b      c   –  School  2:    b      a      c      d   7  
  • 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   Percentage  of  GOK  students  and  na1ve  speakers  across   schools   0.8   0.7   %  GOK  students   0.6   %  Dutch  @  home   0.5   0.4   0.3   0.2   0.1   0   10%  lowest   2   3   4   5   6   7   8   9   10%  highest  
  • Analysis  of  the  current  situa3on  –  distance  to  school   Brussels  kids  going  to  preschool  in  Brussels  -­‐  closest  school   0.35   0.3   whole  sample   0.25   low  socio   high  socio   gok   0.2   Dutch  @  home   0.15   0.1   0.05   0   closest   2  to  3   4  to  5   6  to  10   11  to  15   16  to  20   21  to  30   above  30  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     Weight  on  ε  set  to  0.05  α1GOK  =  0.70  α1rest  =  0.58  δ  =  0.75      
  • Counterfactual  1:  From  3me  to  distance  as  a  3e-­‐breaker  –  aggregate  results  
  • Counterfactual  1:  Distribu3onal  aspects  
  • Counterfactual  1:  Impact  on  school  popula3on   Propor1on  of  Dutch  na1ve  speakers  -­‐  before  and  aQer   0.8   0.7  0.6   0.5  0.4  0.3  0.2   0.1   0   10%   2   3   4   5   6   7   8   9   10  %   lowest   highest   simulated  "before"   "ater"   actual  
  • Impact  on  school  popula3on  (cont’d)   Propor1on  of  GOK  students    before  and  aQer,  per  decile  of  schools   0.8   0.7   0.6   0.5   0.4   0.3   0.2   0.1   0   10%   2   3   4   5   6   7   8   9   10  %   lowest   highest   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   1   0.9   0.8   Propor3on  d’élèves  GOK  par  décile  d’écoles   0.7   0.6   0.5   0.4   0.3   0.2   0.1   0   1er  décile   2   3   4   5   6   7   8   9   10e  décile   Simula3ons  avec  quota  GOK   Simula3ons  sans  quota  GOK   Situa3on  actuelle  
  • Illustra3on:  écoles  gardiennes  NL  de  Bruxelles  2.  Conséquences  redistribu3ves  
  • Illustra3on:  écoles  gardiennes  NL  de  Bruxelles  3.  De  l’importance  de  la  procédure   1   0.9   0.8   0.7   0.6   0.5   0.4   0.3   0.2   0.1   0   1   11   21   31   41   51   61   71   premier  arrivé,  premier  servi   AAD-­‐élèves   AAD-­‐écoles