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