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

1. Topic 3C15 Economic Policy AnalysisEducation: School inputs and pupil performance Kjell G. Salvanes November 10 and November 17, 2003

2. 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?

3. 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?

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

5. School resources and student performanceIs there a connection?

6. 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?

7. 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)

8. 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

9. Causal effect vs correlation • Consider the realtionship between student performance Yi and School resources Si Yi=a+(b+vi)Si+ui Si=1 denotes a small class size, b+vi is the unobserved returns to be in a class with much resources, and ui represents all other individual resources determining performance.

10. Different measures • The expected (average) performance outcome for those in a small class (Si=1): E(YiS=1-YiS=0|S=1)=b+E(vi|S=1) 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, S=1: E(vi|S=1)>0

11. Different measures • Compare those in small classed to those in large classes. E(Yi|S=1)- E(Yi|S=0) =b+E(vi|S=1) + E(ui|S=1)- E(ui|S=0) The last term is the selection bias

12. 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.

13. Methods to solve these problems • Experiments • Construct the assigment such that there is no systematic relationship between class size and students background variables: E(ui|S=1)= E(ui|S=0) • Hence there is no selection bias • However: • Expensive, • Unethical

14. 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

15. 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(ui|Xi,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.

16. 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

17. 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.

18. 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 women’s wages • No impact on men’s wages and eduational attainment

19. 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

20. 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

21. 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

22. 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

23. 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)

24. 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

25. 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

26. 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 experiment:10 cohorts (1948-1957) passing through 2 different school systems • Targeted to certain groups

27. 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)

28. 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)

29. 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?

30. Reform implementation and municipality characteristics • Implementation decided at municipality level, costs reimbursed by the Government • Government’s strategy: reform implementation according to a representative set of municipalities • No signs of selection on municipality observables in our data

31. Data • SN’s 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

32. 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 > 1 year (ii) Commuting between residence and school (iii) Special arrangement for the earliest cohorts (iv) School reform coincides with municipality reform

33. 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 MunicipalityRegister 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

34. 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

35. 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

36. 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 • ________________________________________________________________

37. Predicted pre and post reform educationConditional 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 • _______________________________________________________________

38. 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

39. 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 + (d+ti)Si + ai + Ui log yi = Xib + dSi + qli Si + rli + Ui

40. 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 ΔATE(x) =xi(βl-βl-1)+(rl- rl-1) li ΔTT(x) =xi(βl-βl-1)+(rl- rl-1) li

41. Earnings equations,estimated coefficients

42. 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 • -------------------------------------------------------

43. 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.

44. 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