Topic 3 C15 Economic Policy Analysis Education: School inputs and pupil performance

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Topic 3C15 Economic Policy AnalysisEducation
School inputs and pupil performance

• Kjell G. Salvanes

November 10 and November 17, 2003
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School quality is again on top of the policy
agendaTopics

• Relationship between school inputs (class size,
  eduation of teachers) and student performance
  (scores, wages)
• Do we need more resources or better teachers?
• For which student outcomes does resources matter
  for?
• Does it matter for all students?
• Educational attainment at high school and
  university level is another issue
• Are compulsory school laws necessary?

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Topics cont

• Does privatisaton of schools/universities matter?
• How will increased university fees matter?
• Selective schools or comprehnesive schools?
• How should we evaluate whether school inputs,
  compulsory school laws and educational policy in
  general matter for student outcomes?

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Todays lecture

• The impact of school resources and student
  performance
• Methodological issues
• The impact of compulsory school laws on
  educational attainment wages

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School resources and student performanceIs there
a connection?
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School resources and student performance

• What are we trying to measure
• We have two schools one using a high level of
  resources per students (small classes) and one
  little resoruces.
• Pick two identical students and put one in each
  of the schools and test performance after a year.
• We cannot do this and we end up comparing results
  for students in schools with for instance large
  and small classes.
• How can we estimate the causal effect of school
  resources on student performance?

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School resources and student performance

• Problems
• Too little variation in e.g. class size
• Between 18 and 30 students per class
• Other factors may be important in explaining
  differences in student performance and which is
  correlated with class size
• Teachers use small classes for less able students
• Parents choose neighbourhood based on school
  quality (class size)
• School with small class size may also have other
  benefits (attracting better teachers etc)

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Methods used to evaluate the impact of school
resources

• Experiments
• Randomly assign students to different types of
  schools
• Cannot do usually
• Collect data and evaluate by estimating something
  like
• Achievement preparation families peers
  schools
• 1) Natural experiments Instrumental variables
• 2) Matching

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Causal effect vs correlation

• Consider the realtionship between student
  performance Yi and School resources Si
• Yia(bvi)Siui
• Si1 denotes a small class size, bvi is the
  unobserved returns to be in a class with much
  resources, and ui represents all other
  individual resources determining performance.

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Different measures

• The expected (average) performance outcome for
  those in a small class (Si1)
• E(YiS1-YiS0S1)bE(viS1)
• This measure is called treatment of the treated.
• The second term reflects the way pupils are
  selected into small classes if those who benefit
  most from small classes there is a positive
  correlation between their characterisics, vi, and
  small classes, S1
• E(viS1)gt0

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Different measures

• Compare those in small classed to those in large
  classes.
• E(YiS1)- E(YiS0)
• bE(viS1)
• E(uiS1)- E(uiS0)
• The last term is the selection bias

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Different measures

• The point is the students in large classes may be
  different from the students in the small classes
  in a systematic way such that performance
  differences are attributed to these differences
  in stead of class size.
• Rich /highly educated parents have their children
  in schools with more resources and small classes.

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Methods to solve these problems

• Experiments
• Construct the assigment such that there is no
  systematic relationship between class size and
  students background variables
• E(uiS1) E(uiS0)
• Hence there is no selection bias
• However
• Expensive,
• Unethical

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Other methods

• Natural experiements or IV
• Use information that allocates students to
  schools with large and small resources to avoid
  selection problems
• Problems
• Depending on which instrument is being used to
  decide allocation into different schools, the
  results may only apply for a certain group of
  students

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Other methods

• Matching
• Basically the method is to compare individuals in
  small and large class sizes that are identical on
  observable characteristics Xi
• I.e. assume that for a set of observed
  characteristics X (family background etc), we
  have that
• E(uiXi,Si)jXi
• This means that both the allocation rule deciding
  whether you og to a small or large school or not
  and the impact of that experience depend on
  observable characteristics.

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Measuring heterogeneity in returns to education
in Norway using educational reforms

• Arild Aakvik
• Kjell G. Salvanes
• Kjell Vaage
• University of Bergen
• Norwegian School of Economics and SSB

November 10 and November 17, 2003
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Approaches and results in papers on the reading
list

• Krueger Experimental estimates of education
  production functions
• Class size and test scores
• Method Experiment STAR project in Tennessee
  random assignment of pupils and teachers after
  kindergarten to small (13-17)/regular (22-25)
  schools , stayed for 4 years
• Results
• Effect after one year on standarized tests
• The advantage is kept throughout the 4 years.

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Approaches and results in papers on the reading
list cont

• Dearden, Ferri Meghir
• Method condition on a lot of background
  variables
• Measure educational attainment and wages on class
  size, British data
• Impact on womens wages
• No impact on mens wages and eduational attainment

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Approaches and results in papers on the reading
list cont

• Dustmann, Rajah, van Soest
• Data England and Wales
• Method Controll for background variables
• Measure effect of class size on educational
  attainment and wages
• Find strong impact of class size on the decision
  to stay on in school after 16 and on wages

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Measuring the Effect of a School Reform on
Educational Attainment and Earnings

• Arild Aakvik
• Kjell G. Salvanes
• Kjell Vaage
• University of Bergen
• Norwegian School of Economics, IZA-Bonn and SSB

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Background

• Controversy regarding returns to education
  especially due to selection concerns and
  heterogeneity in returns
• The decision to take more education is a complex
  process.
• ability, financial constraint and preferences are
  usually unobserved for the researcher
  endogeneity problem
• heterogeneity in the return heterogeneity arises
  if individuals select into education based on
  their comparative advantages of education

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• A natural but mainly unexploited resource of
  information to overcome these problems are the
  educational reforms in the European countries in
  the postwar period.
• The focus in the present paper is to exploit some
  interesting features of one of the school reforms
  in Norway - the school reform extending the
  mandatory years of schooling from 7 to 9 years.
• The reform took 10 years to implement and we
  observe same birth cohorts going through both
  compulsory school systems.
• Use additional reforms to identify a Roy model

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• We utilize a flexible framework and a very rich
  data set to study different return parameters of
  education, both in a linear and non-linear
  fashion
• we allow the effect of education to vary both in
  terms of observed and unobserved factors.
• This model is termed a random coefficient model
  where we estimate returns to different levels of
  education (Roy model)

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Overview

• The reform
• The reform as an instrument
• additional identification strategy
• The data
• Effects on educational attainment
• Two model of estimating returns
• Continuous in education
• Using a flexible Roy model for education levels

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Aims of the reform

• Increase the minimum level of education
• Smooth the transition to higher education
• Enhance equality of opportunities along the
  socio-economic and geographical dimensions

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The school reform

• From 7 to 9 years of compulsory schooling Old
  system
• New system
• Implemented from 1959-1974 (1961-1970)
• Impl. at municipality level, decided locally
• Social experiment10 cohorts (1948-1957) passing
  through 2 different school systems
• Targeted to certain groups

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Reforms in other countries

• Similar reforms in Sweden (Meghir Palme, 1999,
  2001), UK (Blundell et al. 1997), France,
  Germany, etc.
• The reform went further in Norway in terms of
  unification and in promoting equality of
  opportunity (Leschinsky and Mayer, 1990)

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Effects of the school reform?

• Are there different educational outcome for
  individuals in the pre vs. post reform system?
• Did the reform help the targeted groups in
  attaining higher education?
• Can we use this (potential) variation to estimate
  the returns to education, i.e. can we use the
  reform as an instrument?
• Using upper secondary reforms/college reform as
  additional instruments (distance to higher
  education)

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The reform as an instrument

• Is the reform correlated with the variable for
  which it serves as an instrument, i.e. did it
  lead to increased educational attainment? For
  all? For some?
• Is the reform uncorrelated with earnings (except
  indirectly through the schooling variable), or
  does it pick up other characteristics of the
  municipalities?

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Reform implementation and municipality
characteristics

• Implementation decided at municipality level,
  costs reimbursed by the Government
• Governments strategy reform implementation
  according to a representative set of
  municipalities
• No signs of selection on municipality
  observables in our data

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Data

• SNs administrative registers earnings, cohort
  and county indicators, work experience, education
  (highest obtained)
• National census of population and housing
  residing municip. during school, family income
  from 1970
• Males in full-time job
• Education and earnings measured in 1995
• Reform dummy
• Availability of high school, college, university
  in the municipality

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Construction of reform indicator

• Use census-data on parents residence in 1960 and
  1970 to assign schooling municipality
• Combine with register-data at municipality level
• Problems
• (i) 20 of the munic. used gt 1 year
• (ii) Commuting between residence and school
• (iii) Special arrangement for the earliest
  cohorts
• (iv) School reform coincides with municipality
  reform

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Construction of reform indicator (continued)

• SN-data on individual reform assignment, but only
  for the group that left school after compulsory
  schooling (16)
• Our strategy Combine Municipality Register and
  SN data, dropping cohorts - but not
  municipalities! - with missing or uncertain
  information
• Use fraction of pupils on reform in the
  municipality as the reform indicator

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School choice

• Continuous (7-20 years)
• Categorical (7 different levels) 1) Pre/post
  reform compulsory school (7/9 years) 2) Upper
  secondary school 1 year mainly vocational 3)
  Upper secondary school 2-3 years mainly
  vocational 4) Upper secondary school 2-3 years
  gymnasium 5) University I, post upper secondary
  school, 1-2 years 6) University II, post upper
  secondary school, 3-4 years 7) University III,
  master level, university degree, 5 years

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O Probit Models of school choice

• Switching regression
• Covariates - Age cohort dummies -
  Municipality variables
• - Parental education
• - Family income (percentiles)
• Derive generalised residuals (li) for the
  earnings equation

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Observed pre and post reform education

• Birth cohorts 1948-57.
• Levels Pre-reform
  Post-reform Change Change in
• __________________________________________________
  ______________
• 1 Pre/post comp. 0.213 0.135 -0.078
  -36.6
• 2 Vocational I 0.167 0.180 0.013
  7.8
• 3 Vocational II 0.249 0.303 0.054
  21.2
• 4 Upper secondary 0.043 0.060 0.017
  39.5
• 5 University I 0.134 0.135 0.001
  0.8
• 6 University II 0.092 0.093 0.001
  1.1
• 7 University III 0.099 0.090 -0.009
  -9.1
• __________________________________________________
  ______________

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Predicted pre and post reform education
Conditional on cohort, region and family income
education

• Birth cohorts 1948-57.
• Levels Pre-reform
  Post-reform Change Change in
• __________________________________________________
  _____________
•  
• 1 Pre/post comp. 0.195 0.141
  -0.054 -27.8
• 2 Vocational I 0.159 0.183 0.024
  15.1
• 3 Vocational II 0.248 0.307 0.058
  23.7
• 4 Upper secondary 0.044 0.060 0.016
  38.3
• 5 University I 0.139 0.133 -0.006
  - 4.5
• 6 University II 0.098 0.089 -0.008
  - 8.9
• 7 University III 0.114 0.084 -0.030
  -26.7
• __________________________________________________
  _____________

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Earnings equations, sources of possible biases

• Unobserved individual heterogeneity
• - ability - financial constraints
• Heterogeneity in returns - self selection
  to education level based on comparative
  advantage
• Non-linearity in returns to education

UNIVERSITY OF BERGEN
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Earnings equations, specifications

• Instrumental Variable ( LATE)
• log yi Xib aSi ai Ui
• log yi Xib aSi rli Ui
• Random Coefficient Model ( ATE)
• log yi Xib (dti)Si ai Ui
• log yi Xib dSi qli Si rli Ui

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The Roy model

• Run the Randdom coefficient model for each
  education level
• E(log yi)Xib aSi rli
• We can then estimate the return to education by
  comparing the different estimated model
  parameters for a given x is simply calculated
  from
• ?ATE(x) xi(ßl-ßl-1)(rl- rl-1) li
• ?TT(x) xi(ßl-ßl-1)(rl- rl-1) li

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Earnings equations,estimated coefficients
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Result from the Roy model

• Table 6.2. Returns to education in percent.
• 
  
• No selection Selection
• -------------------
  -------------------
• ATE TT ATE TT
• --------------------------------------------------
  -----
• 1
• 2 -00.2 01.2 04.8 01.5
• 3 08.3 08.8 08.8 09.2
• 4 20.7 21.1 22.1 21.7
• 5 27.0 26.8 23.8 27.4
• 6 21.8 21.9 15.7 22.7
• 7 44.6 42.3 31.7 43.3
• --------------------------------------------------
  -----

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Main findings

• The reform enhanced educational attainment for
  low achievers
• Pupils from low income families were picked up by
  the reform (?)
• OLS gives biased estimates of the returns to edu.

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Main findings

• Non-linearity in returns to education
• Selection on unobservables appears to be
  important
• Appears to be hard to obtain gains from inducing
  a very high proportion to university education