Education in developing countries
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Education in developing countries,. Michael Kremer Economics 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

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Education in developing countries,

Michael Kremer

Economics 1386, Fall 2006


  • 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

Background: Quantity (II) Primary Schooling

Background: Quantity (III) Average Years of Schooling (Age 15+)

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

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 Africa6.7

  • Latin America1.4

  • OECD1.3

Background: Educational Finance (III) Class Size

Background: Educational Finance (IV) Teacher Training

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

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 (VII) Households help bear education costs

Background: Educational Finance (VIII) Private funding

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

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: Teacher Activity at Time of Observation in India

Absence: Absence rates vs. GDP per capita(for sample countries and Indian states)


Health Workers








Absence rate (%)

Absence rate (%)





















Per-capita income (GDP, 2002, PPP-adjusted)

Per-capita income (GDP, 2002, PPP-adjusted)


Fitted values

Indian states

Absence: Raw figures on distribution of absence across teachers

Absence: Estimated distribution of teacher absence

Absence: Stated Reasons for teacher absence in India

Absence: Multicountry Correlates of Teacher Absence – HLM Estimates

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)

Absence: Multicountry Correlates of Teacher Absence – HLM Estimates (continued)

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)

Absence: Private Schooling and Teacher Absence in India

  • Surveyed private schools in villages visited

    • Teachers have much lower pay

    • More 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)

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


  • 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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