Ponencia del profesor Victor Lavy (Universidad hebrea de Jerusalem): Expanding School Resources and Increasing Time on Task: Effects of a Policy Experiment in Israel on Student Academic Achievement and Behaviour

1. Do Differences in Schools' Instruction Time
Explain International Achievement Gaps?
Evidence from Developed and Developing
Countries
Victor Lavy
Hebrew University, University of Warwick, and
NBER
October 2013

2. Large differences across countries in instructional
time in public schooling institutions
•
Children aged 15:
– Belgium, France and Greece – Over 1,000 hours per year in secondary schools
– England, Luxembourg and Sweden - 750 hours, Spain - 833
•
Children aged 7-8:
– England, Greece, France and Portugal - over 800, Spain – 959
– Finland and Norway - less than 600
•
These differences also reflected in number of lessons (measured in hours)
per week in different subjects, for example children aged 15:
– Denmark: math - 4.0, reading - 4.7, science - 2.8
– France: math - 3.4, reading - 2.5, science - 3.6
– Austria: math - 2.7, reading - 2.4, science - 2.2
– Spain: math - 3.1, reading - 3.2, science - 2.8

3. Table 1 - Means and Standard Deviations of Instructional Time in OECD, Eastern European, and Developing
Countries, 2006
Proportion of pupils by weekly instruction time
Subject
Mean Value
Std. Dev
< 2 Hours
2-3 Hours
4-5 Hours
6 Hours +
Panel A: 22 OECD Countries
All Subjects
3.38
(1.48)
13.16
40.43
36.45
9.97
Math
3.53
(1.38)
8.72
39.54
43.14
8.60
Science
3.06
(1.57)
21.14
42.72
25.53
10.61
Reading
3.54
(1.44)
9.61
39.02
40.66
10.71
Panel C: 13 Developing Countries
All Subjects
3.23
(1.71)
22.86
34.72
27.51
14.90
Math
3.48
(1.69)
18.72
30.73
34.06
16.50
Science
2.97
(1.74)
29.03
37.17
18.53
15.27
Reading
3.24
(1.65)
20.85
36.27
29.94
12.95

4. Table 1B : Descriptive Statistics - Test Score and Instructional Time
Test scores
Instructional time
OECD Eastern Developi OECD Eastern Developi
Develop Europe
ng
Develop Europe
ng
Mean
513.4
485.6
413.5
3.38
3.05
3.23
Standard Deviation
84.4
between pupils
86.9
75.1
1.02
0.88
1.22
Standard Deviation
38.8
within pupils
40.9
46.7
1.08
1.28
1.19

5. Questions of Interest:
• Can these differences explain differences across countries in
achievements in different subjects?
• Can differences in the average productivity of instructional
time explain the performance differences between pupils from
different countries?
• What characteristics of schools can explain variations in the
average productivity of instructional time?

6. Scientific Background
•
There is convincing evidence about the effect of several inputs in
the education production function:
– Class size, Teachers’ training and certification, Remedial education,
Teachers quality, Computer aided instruction, School choice, Tracking,
Gender and ability peer effects, Students’ incentives, Teachers’
incentives….
•
There is limited evidence on effect of classroom instructional time.
•
Important evidence because Instructional Time can be increased
relatively easily.
•
There is much scope for such an increase in many countries.

7. Related literature
•
Effects of the length of the school year:
– Grogger (1997), Eide and Showalter (1998), in the US, found
insignificant effects
– Rizzuto and Wachtel (1980), Card and Krueger (1992), Betts and
Johnson (1998) used State data in US, positive effects
– Lee and Barro (2001) examine effect in cross section of countries,
find no effects
– Wößmann (2003), cross-country data, negligible effect
– Pishke (2008), use German short school years in 1966-67 as a
natural experiment, find increased repetition, fewer students
attending higher secondary school tracks, no adverse effect on
earnings and employment later in life.
– Dolton and Marcenaro-Gutierrez (2009), focus on effect of
teachers’ salaries but include teaching hours per year. Report
inconclusive evidence. With PISA data it is negative or zero.

8. This Study
•
Investigates the causal relationship between instructional time (IT) and pupils'
knowledge in math, science and reading.
•
Examines factors that explain part of the variation across countries in the
average productivity of IT.
•
PISA 2006 data for 50+ countries, measured skills of 15-year-olds, variation in
IT across subjects.
•
Exploits within student variation in t-scores and IT across subjects.
•
Estimate pupil fixed effect models, implicitly also control for family, school,
community and country fixed effects.
•
Investigate whether the estimated effect of IT varies by certain characteristics of
the school: accountability, autonomy, environment, labor market for teachers.

9. Preview of Findings
• Instructional Time has a positive and significant effect on the
academic achievements of 15 years old pupils in OECD
countries
• Almost identical results from the Israeli data of pupils in 5th grade
• Estimates from Eastern European countries are very similar
• In a sample of developing countries much lower effect of
Instructional Time, half of the OECD estimate

10. Preview of Findings
• Overall, the effect is larger for girls, for pupils from
disadvantaged backgrounds and for immigrants.
• Effect of instructional time is larger when:
– More school accountability measures are adopted
– More school autonomy in hiring teachers and determining
their wages,
– More school autonomy in using their budget
• Effect of instructional time does not vary with:
– School autonomy in pedagogy
– Quality of inputs such as computers and other school
facilities

11. Data
•
PISA “- Program for International Student Assessment, 2000, 2003 and 2006 (58
countries)
•
Random sample of 15-year-olds, between 15 years 3 months and 16 years 2
months of age at the time of the test , mostly near end compulsory school
•
Measures student performance in reading, mathematics and science literacy,
pencil-and-paper tests, both multiple-choice questions and questions requiring
students to construct their own responses
•
The material is organized around texts and sometimes includes pictures, graphs
or tables setting out real-life situations, about seven hours of test material
•
From this, each student takes a two-hour test, with the actual combination of test
materials different for every student
•
The average score among OECD countries is 500 points and the standard
deviation is 100 points.

12. Identification of the Effect of Instructional time
•
The effect of IT is usually confounded by the effects of unobserved
correlated factors.
– If self-selection and sorting of students across schools are affected by school
resources
– If there is a correlation between school IT and other characteristics of the school that
may affect students’ outcomes.
•
One possible method to account for both sources of confounding
factors in the estimation of IT is to rely on within-student variations in IT
across subjects:
– Examine whether within student differences in t-scores and in school IT are
systematically correlated.
– The basic idea for identification is that the student’s characteristics,
average ability, and the school environment are the same for all three
subjects except for the fact that some subjects have more instructional time
than the other subjects do.
– It could be that at the school level, such variation is not purely random but
the cause of such selection does not vary within each student.
– Threats for identification: student subject specific ability and other inputs
correlated with subject specific instructional time.

13. Based on this approach I estimate following equation:
Aijk = µi + γ Hjk + β Xijk + δ Sk + (εj + ηk)+ uijk
Where:
Aijk is the achievment of the ith student in the jth school in the kth subject
Hjk is the instructional time in the jth school in the kth subject
Xijk is a vector of pupil characteristics
Sj is a vector of subject dummies
εj and ηk are unobserved characteristics of the pupil and the school
uijk is the remaining unobserved error term

14. Table 2 - OLS Regressions of Test Scores on Instructional Time, OECD Sample
Mathematics
Science
Reading
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
21.69
(1.03)
27.98
(1.19)
24.45
(1.10)
26.24
(0.80)
38.36
(0.90)
33.92
(0.85)
4.56
(1.00)
15.43
(1.32)
12.48
(1.19)






I. Continuous Hours:
Hours
Country dummies
Individual
characteristics




15. Table 3 - Estimated Effect of Instructional Time on Test Scores, OECD Sample
Whole Sample
OLS
(1)
Student FE
(2)
A. Mathematics + Science + Reading
Regression I.
Hours of instruction
19.58
(0.72)
Number of students
5.76
(0.37)
460,734
B. Mathematics + Science
Regression I.
Hours of instruction
Number of students
25.48
(0.73)
7.14
(0.55)
307,156

16. The effect size of the instructional time
•
The standard deviation of the within student distribution of instructional
time is 1.0
•
The standard deviation of the within student test scores distribution is
38.0
•
One standard deviation change in the within student distribution of
hours will cause an increase of a 5.76 points
•
This change is equal to 0.15 of a standard deviation of the within
student test score distribution or 0.07 of a standard deviation of the
between student test score distribution.

17. Robustness checks
•
Estimated effect of hours is higher in sample of private school
•
Results are unchanged when a control is added for subject specific lack of
qualified teachers
•
Results are very similar when controls are added for extent to which
admission to school depends on academic ability
•
No significant differences by samples stratified by the importance of
academic ability for schools' admission policies [test scores are prerequisite
or high priority for admission, test scores are considered for admission, test
scores are not considered for admission]
•
No variation in effect across samples stratified by school tracking practices

18. Table 3 - Estimated Effect of Instructional Time on Test Scores, OECD Sample
Whole Sample
Sample Divided by School Admission Policy
Academic Record is
Irrelevant
(1)
(2)
Academic Record
Taken into Account
(3)
A. Mathematics + Science + Reading
Hours of instruction
5.76
(0.37)
6.01
(0.50)
6.21
(0.89)
Number of students
460,734
266,769
86,370

19. Table 4 - Estimated Effect of Instructional Time on Test Scores
by School Tracking Policy
Track in School
Hours of
instruction
Number of students
Track In Class
No Tracking
6.61
6.17
5.17
(0.53)
(0.56)
(0.68)
201,138
160,188
212,169

20. Table 5 - Estimated Effects of Instruction Time on Test Scores, with Controls Included in the
Regressions for Special Science Activities in School and for Scarcity of Teachers in Each
Subject
Control Added For
Special Science
Scarcity of Teachers in
School Activities
Each Subject
Hours of instruction
5.59
(0.39)
5.75
(0.37)
Number of students
460,734
224,508

21. Table 6 - Estimated Effect of Instructional Time on Test Scores, by Gender, OECD Sample
Boys
Girls
Student
OLS
FE
OLS
Student FE
(1)
(2)
(3)
(4)
Hours of instruction
Number of students
20.25
(0.86)
4.99
(0.40)
224,508
18.62
(0.77)
5.62
(0.41)
236,226

22. Table 7 - Heterogeneity in Student Fixed Effect Regressions of Test Scores on
Instructional Time, OECD Sample.
High Parental
Education
Hours of
Instructional
Time
Low Parental
Education
Immigrants First
Immigrants Second
4.83
6.54
6.37
7.62
(0.42)
(0.44)
(0.88)
(0.95)

23. Table 6 - Estimates using OLS and Pupil Fixed Effects,
Samples of Eastern European and Developing Countries
High
Low
Immigra
Parental Parental
nt 1st
All
Boys
Girls
Educatio Educatio
Gen.
(1)
(2)
(3)
(4)
(5)
(6)
Eastern European Countries
Immigra
nt 2nd
Gen.
(7)
OLS
38.20
(1.28)
38.89
(1.42)
37.25
(1.38)
41.20
(1.56)
33.37
(1.25)
26.35
(3.32)
35.68
(2.70)
Fixed Effects
6.07
(0.56)
5.15
(0.59)
6.49
(0.59)
5.03
(0.66)
6.67
(0.62)
5.53
(2.07)
7.26
(1.88)
177,015
84,612
92,403
78,006
99,009
3,525
5,604
Number of Students

24. Table 6 - Estimates using OLS and Pupil Fixed Effects,
Samples of Eastern European and Developing Countries
High
Low
Immigra
Parental Parental
nt 1st
All
Boys
Girls
Educatio Educatio
Gen.
(1)
(2)
(3)
(4)
(5)
(6)
Immigra
nt 2nd
Gen.
(7)
Developing Countries
36.60
Fixed Effects
Number of Students
35.24
43.27
29.64
58.13
51.54
(1.20)
(1.36)
(1.24)
(1.38)
(1.23)
(5.34)
(4.15)
2.99
2.39
3.29
3.41
2.60
18.59
11.11
(0.80)
OLS
38.17
(0.87)
(0.90)
(0.94)
(0.88)
(4.65)
(3.91)
238,938
108,927
130,011
76,970
82,322
1,642
2,210

25. Results from primary and middle schools in
Israel
Table 8 - OLS and Pupil Fixed Effects in Israel Using Various Combinations of Pooled Subjects
5th Grade
8th Grade
Math Math Science All 3 Math Math Science All 3
&
&
& Subject &
&
& Subject
Science English English
s
Science English English
s
Sample
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
All
OLS 0.075 0.082 0.058 0.071 0.037 0.090 0.010 0.036
(0.008) (0.011) (0.008) (0.007) (0.010) (0.017) (0.010) (0.010)
FE 0.055 0.060 0.060 0.058 0.041 0.036 0.015 0.029
(0.010) (0.016) (0.012) (0.007) (0.012) (0.024) (0.015) (0.009)

26. Interaction effects of productivity of school instruction time
with structural characteristics of the school
•
Effect of instructional time is larger when:
– More school accountability measures are adopted
– More school autonomy in hiring teachers and determining their
wages
– More school autonomy in using their budget
– School governing board influence budget and staffing

27. Interaction effects of productivity of school instruction time
with structural characteristics of the school
•
Effect of instructional time does not vary with:
– School autonomy in pedagogy
– Quality of inputs such as computers and other school facilities
– Governing board influence on curriculum and evaluation
methods

28. Table 10 - Estimated Effects of School Characteristics Interacted with Instructional Hours,
OECD Countries.
Separate Spec.
Joint Spec.
Index
Hours Hours Hours Hours
Means
Main
X
X
X
Index
(1)
(2)
(3)
(4)
(5)
Achievement data are posted publicly (e.g.
in the media). (Binary Variable).
.335
(.472)
5.017
(.447)
2.744
(.840)
1.962
(.903)
2.452
(.912)
Achievement data are used in evaluation of
the principal's performance (Binary
.216
(.411)
5.153
(.432)
2.106
(.889)
2.158
(1.135)
2.317
(1.134)
Achievement data are used in evaluation of
teachers' performance (Binary Variable).
.294
(.456)
5.501
(.458)
.345
(.819)
-1.230
(1.015)
-.934
(1.010)
Students are Grouped by Ability into
Different Classes -all or some subjects-
.461
(.498)
5.222
(.517)
1.385
(.744)
.498
(.784)
-.168
(.806)
Students are Grouped by Ability within
their Classes -all or some subjects- (Binary
.437
(.496)
5.398
(.508)
.771
(.753)
.303
(.778)
.408
(.778)
Quality of Educational Resources: Index,
(Range -3.45 to 2.1)
.150
(.989)
5.834
(.395)
.099
(.393)
.435
(.399)
.442
(.400)

29. Table 10 - Estimated Effects of School Characteristics Interacted with Instructional
OECD Countries.
Separate Spec.
Joint Spec.
Index
Hours
Hours
Hours
Hours
Means
Main
X
X
X
Index
(1)
(2)
(3)
(4)
(5)
School Responsibility for Resource
Allocation: Index, (Range -1.1 to 2.0)
School Responsibility for Curriculum &
Assessment: Index (Range -1.4 to 1.3).
-.058
(.946)
5.925
(.380)
1.224
(.398)
.842
(.433)
.938
(.435)
.052
(.964)
5.830
(.386)
-.247
(.399)
-.451
(.427)
-.561
(.429)
School Governing Board Influences
Staffing (Binary Variable).
School Governing Board Influences
Budget (Binary Variable).
.363
(.481)
4.981
(.523)
2.599
(.763)
1.199
(.883)
.706
(.455)
3.759
(.711)
2.974
(.843)
1.834
(.925)
School Governing Board Influences
Instructional Content (Binary Variable).
.162
(.368)
5.973
(.429)
-.588
(.968)
-.199
#####
School Governing Board Influences
Assessment (Binary Variable).
.219
(.413)
6.018
(.464)
-.837
(.831)
-.802
(.922)
Hours Main Effect
4.676
(.713)
3.255
(.964)

30. Conclusions
•
Instructional time has positive significant effect on test scores.
•
The OLS results are highly biased upward but the within student
estimates are very similar across groups of developed and middleincome countries and age groups.
•
Effect size of one more hour of instruction:
– 0.15 of standard deviation of the within student standard
deviation in test scores.
•
The estimated effect of instructional time in the developing countries
is only half of the effect size in the developed countries.

31. Conclusions
•
There is significant association between characteristics of the work
environment of teachers and of the education system in OECD
countries and the average productivity of instructional time.
•
These correlations point to some directions of how productivity can
be improved in some of the developed and in less developed
countries.
•
For example:
– Enhance school accountability measures
– Increase school autonomy in hiring teachers/determining wages
– Increase school autonomy in using own budget
– Allow school governing board influence budget and staffing