education in developing countries, michael kremer economics 1386, fall 2006
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Education in developing countries,
Michael KremerEconomics 1386, Fall 2006
Outline
Background: Education in Developing Countries Methodology Reducing the Cost of Education Changing Education Behavior Improving Provision of Education
Inputs Incentives for Providers
Changing the Interaction of Consumers and Providers
Local Control and Participation Contracting and Choice
Conclusion
Background: Motivation
Widely held belief that education can play a critical role in development Macro- impact of education on economic growth
Lucas (1988), Barro (1991), Mankiw et al. (1992) Causal relationship (Pritchett, Bils & Klenow)
Returns: old OLS literature, new IV literature Psacharopoulos 1985; Duflo 2001
Adoption of new technologies Foster and Rozenzweig (1996)
Means to improve health, reduce fertility Schultz (1997), Strauss and Thomas (1995)
Education as an intrinsic good Sen (1999)
Background: Motivation
Development policy makers also enthusiastic about education 2 of the 8 Millennium Development Goals
Universal primary education Gender equity at all education levels
Background: Motivation
Rich set of experiences to examine Wide variation in input levels and education
systems across developing countries In recent years, dramatic policy changes and
reforms in many developing countries In last 10 years: many randomized
evaluations of education policies (rare in developed countries)
Background: QuantityGross Enrollment Growth
1960 2000
Primary Low income 65 102
Middle income 83 110
High income 109 102
Secondary Low income 17 54
Middle income 21 77
High income 63 101
Background: Quantity (II) Primary Schooling
2000 net enrollment
1999 grade 4 completion
Low-income 85 80
Middle-income 88 88
High-income 95 98
Background: Quantity (III) Average Years of Schooling (Age 15+)
1960 2000 Change
Low income 1.6 5.2 3.6
Middle income 2.8 5.9 3.1
High income 7.4 10.1 2.7
Source: Barro and Lee (2000)
Background: Quantity (IV)Room for Improvement
1 of 4 adults in developing countries illiterate UNESCO (2002)
Today 113M primary age children not in school UNDP (2003); UNESCO (2002)
4 out of 10 primary-age children in sub-Saharan Africa do not go to school In Niger, only 26% of primary-age children go to
school UNESCO (2003)
Background: Educational Finance Government Expenditures on Education
Expenditure as % of GDP
Expenditure per student as % of GDP per capita
Primary Secondary Primary Secondary
Low-income 1.0 1.1 7.0 16.7
Middle-income 1.8 1.4 13.3 15.5
High-income 1.4 1.9 18.8 21.5
Background: Educational Finance (II) Government Expenditures and Teachers
Teacher salaries 74% of recurrent expenditures (Bruns et al. 2003)
Teacher salary/ per-capita GDP Sub-Saharan Africa 6.7 Latin America 1.4 OECD 1.3
Background: Educational Finance (III) Class Size
Pupil-teacher ratio
Primary Secondary
Low-income 32 25
Middle-income
25 20
High-income 16 14
Background: Educational Finance (IV) Teacher Training
% Trained Teachers
Primary Secondary
Low-income 90 69
Middle-income
90 83
High-income - -
Background: Educational Finance (V)
In many developing countries: School systems are highly centralized Teachers’ unions are strong Teacher incentives are weak
Background: Educational Finance (VI)Centralized Education, Heterogeneous Needs
Heterogeneity within developing countries Educational background School quality Language
Makes designing single curriculum for all students difficult
Background: Educational Finance (VII) Households help bear education costs
Sometimes households pay for private schools Sometimes parents pay costs at public schools
Parents must provide basic school inputs (e.g. textbooks, uniforms)
Some costs are collective responsibility of parents (e.g. school roof)
Some costs are passed on through official or unofficial school fees
Background: Educational Finance (VIII)
Private funding
Per-pupil primary school spending $US
Government Private
Jamaica $221 $178
Philippines $110 $309
Vietnam $23 $14
Quality of Education
Lack of basic equipment and supplies Textbooks: only 20% of Kenya primary students had
their own (recent changes) Blackboards: lacking in 39% schools in rural northern
Vietnam Building: lacking in 8% of schools in India
Quality (II)PISA Study: Mathematics and Reading Achievement of 15-year-olds
Country Mean math score Mean reading score % very low skills
France 517 505 4.2
Japan 557 522 2.7
UK 529 523 3.6
US 493 504 6.4
Argentina 388 418 22.6
Brazil 334 396 23.3
Chile 384 396 23.3
Indonesia 367 371 31.1
Mexico 387 422 16.1
Peru 292 327 54.1
South Korea 547 525 0.9
Thailand 432 431 10.4
Quality (III)Quality even lower in low-income countries
Bangladesh: 58% of rural children 11 and older failed to identify 7 of 8 presented letters Greany, Khandker and Alam (1999)
India: 36% of 6th graders unable to answer: “The dog is black with a white spot on his back and one white leg. The color of the dog is mostly: (a) black, (b) brown, or (c) grey” Lockheed and Verspoor (1991)
Quality: Teacher Absence
Chaudhury, Hammer, Kremer, Muralidharan and Rogers
Survey methods Absence rates across countries and sectors Concentration of absence Correlates of absence Institutional forms Conclusion
Teacher Absence: Sampling
Unannounced visits to public primary schools, health centers
Bangladesh, Ecuador, Indonesia, Peru and Uganda: ~100 schools, ~1000 teachers, 2000+ observations
per country India sample is much larger
3,750 schools, 16,500 teachers, ~50,000 observations
Teacher Absence: Survey Methodology and Absence Definition
Measurement: Direct observation of each teacher, not administrative records
Definition of absence: Teacher was considered absent if he/she could not be found anywhere in
the school Excluded from the sample: part-time teachers; teachers reported as “on
another shift” Exclude cases where the school is closed due to:
Official/Scheduled Holidays Bad weather (rain, heat wave) Construction/repairs School Functions (Sports day, picnics, exams)
Absence: Multi-country Results on Extent of Absence
Absence rates (%) in: Primary SchoolsPrimary Health
Centers
Bangladesh 16 35Ecuador 14 --India 25 40Indonesia 19 40Papua New Guinea 15 --Peru 11 23Uganda 27 37Zambia 17 --
25
Absence: Teacher Activity at Time of Observation in India
Teacher Observation% of
Observations
In class, teaching 45.0
In class, not teaching 5.9
In school, idle/on a break 9.5
Doing administrative work 6.2
Accompanying the surveyor 8.7
Can’t find the teacher (School Open) 19.4
Can’t find the teacher (School Closed) 5.2
Others 0.2
Absence: Absence rates vs. GDP per capita
(for sample countries and Indian states)
BNGECU
IDN
PER
UGA
020
4060
Ab
sen
ce r
ate
(%)
6.5 7 7.5 8 8.5Per-capita income (GDP, 2002, PPP-adjusted)
Teachers
BNG
IDN
PER
UGA
020
4060
Ab
sen
ce r
ate
(%)
6.5 7 7.5 8 8.5Per-capita income (GDP, 2002, PPP-adjusted)
Health Workers
Countries Fitted values
Indian states
Absence: Raw figures on distribution of absence across teachers
0 1 2 3
Bangladesh 73.4 23.5 3.2 --
Ecuador 82.8 6.9 10.4 --
India 49.1 32.7 13.5 4.8Indonesia 67.7 27.5 4.8 --
Peru 81.0 17.3 1.7 --
Uganda 63.0 29.6 7.4 --
Percentage of teachers who were absent this many times in 2 visits (3 visits in India)
Absence: Estimated distribution of teacher absence
%age of providers
%age of absences
%age of providers
%age of absences
%age of providers
%age of absences
Bangladesh 63.1 41.4 36.4 57.1 0.5 1.5 0.31 0.37Ecuador 80.6 9.7 5.1 11.0 14.3 79.4 0.78 0.92India 48.4 18.9 40.5 53.1 11.2 28.0 0.41 0.55Indonesia 61.5 22.4 28.9 46.8 9.6 30.8 0.52 0.64Peru 80.3 38.0 16.8 46.2 2.9 15.8 0.60 0.68Uganda 55.6 13.1 24.4 31.4 20.1 55.5 0.56 0.75Mean (unweighted) 64.9 23.9 25.3 40.9 9.8 35.2 0.53 0.65
Raw Adjust.
Absence Gini Providers with underlying probability of absence in this range:Pr(absence)<20% 20%<=Pr(absence)<50% Pr(absence)>=50%
29
Absence: Stated Reasons for teacher absence in IndiaSchool Closed % of Observations
Teachers have not yet come 1.0%
Local/Other Holiday 1.0%
School Closed Early 0.7%
Teachers Meeting/Training 0.6%
Other Government Work 0.1%
Don't Know/Others 1.7%
Teacher cannot be found
Authorized/Informed Leave 6.7%
Official Teaching Related Duty 4.9%
Sick 1.5%
Official Non Teaching Duty 0.8%
Not yet arrived 0.6%
Left Early 0.6%
Uninformed Absence/Don't Know/Others 4.3%
Absence: Multicountry Correlates of Teacher Absence – HLM Estimates
Coefficient Standard error
Male 1.942** 0.509 BNG, ECU, IND***, IDN, PER
Ever received training 2.141 4.354 BNG, ECU***, PER
Union member 2.538* 1.258 ECU***, IND, IDN, PER
Born in district of school -2.715** 0.833 BNG, ECU, IND***, IDN*, PER, UG
Received recent training -0.74 2.070 BNG, ECU***, UGA
Tenure at school (years) 0.033 0.044 BNG, IDN, PER
Age (years) 0.021 0.046 ECU, IND, UGA*
Married 0.742 0.972 BNG, IDN, UGA**
Contract teacher 5.722 2.906 ECU, IDN**, PER (n/ a BNG/ UGA)
Has university degree -1.055 1.162 ECU, IDN
Has degree in education 1.806 2.071 ECU**, IND*
Head teacher 3.771*** 0.888 BNG, ECU, IND***, IDN**, PER, UGA
HLM estimates for the multicountry sample Countries where coefficient has same
sign as multicountry coefficient
31
Absence: Indian teachers
More powerful teachers absent more Older teachers (1% more for every 10 years) More educated teachers (2-2.5% more with a
college degree) Head teachers (4-5% more) Males (1.5-2% more)
Teacher pay (within scale, across states)
Absence: Multicountry Correlates of Teacher Absence – HLM Estimates (continued)
Coefficient Standard error
Male 1.942** 0.509 BNG, ECU, IND***, IDN, PER
Ever received training 2.141 4.354 BNG, ECU***, PER
Union member 2.538* 1.258 ECU***, IND, IDN, PER
Born in district of school -2.715** 0.833 BNG, ECU, IND***, IDN*, PER, UG
Received recent training -0.74 2.070 BNG, ECU***, UGA
Tenure at school (years) 0.033 0.044 BNG, IDN, PER
Age (years) 0.021 0.046 ECU, IND, UGA*
Married 0.742 0.972 BNG, IDN, UGA**
Contract teacher 5.722 2.906 ECU, IDN**, PER (n/ a BNG/ UGA)
Has university degree -1.055 1.162 ECU, IDN
Has degree in education 1.806 2.071 ECU**, IND*
Head teacher 3.771*** 0.888 BNG, ECU, IND***, IDN**, PER, UGA
HLM estimates for the multicountry sample Countries where coefficient has same
sign as multicountry coefficient
Absence: Multicountry Correlates of Teacher Absence – HLM Estimates (continued)
Coefficient Standard error
School inspected in last 2 mos. -0.142 1.194 BNG, ECU, IND***, UGASchool is near Min. Education office -4.944 2.642 BNG, ECU***, IND**, IDN*
School had recent PTA meeting 2.308 1.576 BNG, ECU, PER*Students' parents' literacy rate (0-1) -9.361*** 1.604 BNG, ECU, IND***, IDN, PER**School infrastructure index (0-5) -2.234*** 0.438 BNG, ECU*, IND***, IDN, PER
School is near paved road 0.040 1.106 BNG, ECU, IDN, UGASchool's pupil-teacher ratio -0.095 0.080 BNG, ECU*, IDN, UGASchool is in urban area 2.039 1.441 ECU, IND, PER
School's number of teachers 0.015 0.113 ECU, PER, UGASchool has teacher recognition program 0.168 3.525 BNG, IND, IDN***, UGADummy for 1st survey round 2.938 1.874 BNG, ECU***, IND***, PER*, UGA
Constant 32.959*** 1.963 BNG***, ECU, IND***, IDN**, PER**, UGA
Observations 34880
* significant at 10%; ** significant at 5%; *** significant at 1%Regressions also included dummies for the days of the week (not reported here).
HLM estimates for the multicountry sample
Countries where coefficient has samesign as multicountry coefficient
34
Absence: School Conditions
Better infrastructure is associated with significantly lower absence
Infrastructure Index from 0-5, which includes existence of covered classrooms, non-mud floors, teachers’ toilet, electricity connection, library
In India, each measure significant on its own, average impact of 1.4% for each 1 point increase in the index
In multicountry sample, correlation is even larger quantitatively and highly significant, at over 2% for each 1 point increase
Absence: Multicountry Correlates of Teacher Absence – HLM Estimates (continued)
Coefficient Standard error
School inspected in last 2 mos. -0.142 1.194 BNG, ECU, IND***, UGASchool is near Min. Education office -4.944 2.642 BNG, ECU***, IND**, IDN*School had recent PTA meeting 2.308 1.576 BNG, ECU, PER*Students' parents' literacy rate (0-1) -9.361*** 1.604 BNG, ECU, IND***, IDN, PER**School infrastructure index (0-5) -2.234*** 0.438 BNG, ECU*, IND***, IDN, PERSchool is near paved road 0.040 1.106 BNG, ECU, IDN, UGASchool's pupil-teacher ratio -0.095 0.080 BNG, ECU*, IDN, UGASchool is in urban area 2.039 1.441 ECU, IND, PERSchool's number of teachers 0.015 0.113 ECU, PER, UGASchool has teacher recognition program 0.168 3.525 BNG, IND, IDN***, UGADummy for 1st survey round 2.938 1.874 BNG, ECU***, IND***, PER*, UGAConstant 32.959*** 1.963 BNG***, ECU, IND***, IDN**, PER**, UGA
Observations 34880
* significant at 10%; ** significant at 5%; *** significant at 1%Regressions also included dummies for the days of the week (not reported here).
HLM estimates for the multicountry sample
Countries where coefficient has samesign as multicountry coefficient
Absence: Private Schooling and Teacher Absence in India
Surveyed private schools in villages visitedTeachers have much lower payMore likely to be fired for absence
Indian private school absence about 2 percentage points lower in sum stats, baseline multivariate regression.
8 percentage points lower with village fixed effects Absence in public schools high in villages with private schools.
Explanations?
Absence: Private Schooling and Teacher Absence in India (continued)
Head Deputy head Permanent
/regular Contract/ informal
Public schools
Private aided
Private schools
Teacher absence rate 30.2% 22.2% 23.1% 24.0% 24.8% 20.1% 22.8%
Number of observations 7117 1979 23333 2037 34918 3371 9098
Absence Rate by Teacher Rank and School Type in India
School typeTeacher rank and appointment type
(public schools only)
38
Absence: Correlation with education outcomes in India
Teacher absence is a significant (but weak) predictor of lower student attendance
A 10% increase in teacher absence is associated with a 1.8% decrease in student attendance
Teacher absence is also a significant predictor of lower student test scores
We conducted a simple 14-question test (2 Verbal, 12 Math) to a randomly selected sample of 10 4th grade children in the schools that we covered
A 20% decrease in teacher attendance is associated with a 2% decrease in test scores
Absence: Multi-country correlates of health worker absence
Absence: Why is Absence So High?
High levels of absence are not efficient – no coordination Technically possible to monitor attendance
Logbook/HM/inspection system Duflo and Hanna (2005) cameras
Political economy In some authoritarian, colonial regimes, absence
reportedly been less of a problem Not an electoral issue Powerful often outside public system Tradeoff between political and civil service systems
Absence: Conclusions One in five teachers is absent, on average Institutional failure Evidence from randomized evaluations
Teacher incentives in Kenya (Glewwe, Ilias, Kremer) Merit scholarships (Kremer, Miguel, Thornton) Cameras in Indian NGO schools (Duflo and Hanna)
Range of interventions could be tested: Improve facilities Intensify and upgrade inspections Empower school committees Publicize absence statistics Increase choice
Outline
Background: Education in Developing Countries Methodology Reducing the Cost of Education Changing Education Behavior Improving Provision of Education
Inputs Incentives for Providers
Changing the Interaction of Consumers and Providers
Local Control and Participation Contracting and Choice
Conclusion
113 million children not in school
• How expensive to address?
• Is their labor needed by household?
• Debate on user fees in health and education• Impact on provider• Impact on consumer
• Strong ideological component to debate, need for evidence
MethodsProblem: Omitted Variable Bias
yi = α+δdi+Xiβ+εi
We want to know δ, the effect of di on yi
Xi is a vector of observable factors, and εi contains the unobserved factors determining yi
If εi is correlated with di, OLS estimate of δ will be biased. Its impossible to be certain because we can’t
observe εi!
Methods (II)Solution: Instrumental Variables
Instrumental variables (IV) can address the omitted variables problem
An instrument zi must be correlated with di and uncorrelated with εi
Methods (III)Solution: IV with Random Assignment
Randomly altering di for some individuals provides an instrument we can be confident in
zi = 1 for individuals who had their di randomly decreased and zi = 0 otherwise.
We know E(ziεi)=0 because randomization ensures it
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