5 Education for All Development Issues in Africa Spring 2007

Contents –Investments in education: Concepts –Education in Africa: Overview –Evaluations of Randomized Experiments –Education of Orphans –Research Example: “Orphaned children and young adults in rural Uganda” by Yamano, Shimamura, and Sserunkumma (2004)

Source: Todaro & Smith, Economic Development 8th, 2003 Age Earnings Primary graduates Secondary graduates Direct costs Opportunity costs Additional benefits Costs Investments in Additional Education Retirement Completion Beginning of 2 nd School

Investments in Additional Education The decision to make investments in additional education depends on the (expected) additional benefits and the sum of costs in additional education. The additional benefits is the sum of the discounted additional life-time earnings: The costs of the additional education include the direct costs of the additional education and the foregone income during the education period (t=1,.., M). When B is larger than C, people decide to invest in the additional education.

Source: Todaro & Smith, Economic Development 8th, 2003 Expected Private Returns Private vs. Social Benefits and Costs of Education Private Costs Years of schooling Social costs Social returns X*

School Supply In the previous figure, the government decides the supply of schools at X*. But because the expected private return is higher than the private cost at this level, there will be more students than the schools can accept. Need to hold entrance exams. Should the government to transfer some of the costs to private (cost-sharing)?

School Enrollment: Overview 1970 Male 1970 Female 1980 Male 1980 Female 2000 Male 2000 Female Sub-Saharan Africa South Asia East Asia & Pacific Europe & C. Asia Latin America Source: World Bank Development Indicator 2004 Gross Enrollment rate in primary education: (Number of pupils in primary education)/ (Number of primary education age children). This number could be higher than 100 if many children repeat the same grade or non-primary education age children enter primary education.

Source: World Bank Development Indicator 2004

Attainment Profile: Tables Ever enrolledCompleting 5 th grade Completed 9th Grade West and Central Africa East and Southern Africa South Asia Central America East Asia and the Pacific South America Europe and C. Asia Source: Pritchett (2004)

Attainment Patterns Grade Primary Secondary Grade Primary Secondary Grade Primary Secondary 1 Proportion of 15 to 19 years olds who have completed each grade The enrollment rate is high initially; the drop-out is severe. The enrollment rate is low initially; the drop- out rate is low. The enrollment rate is high initially and remains so. The transition is low.

Source: Pritchett (2004) Attainment Patterns by Wealth Grade Primary Secondary Grade Primary Secondary Grade Primary Secondary 1 Proportion of 15 to 19 years olds who have completed each grade The drop-our rate is higher for the poor than the non-poor. The enrollment rate is low for the poor; the drop-out rate is low for both. The enrollment rate is high for both and remains so. The transition is low for the poor. Non-Poor Poor

Source: Nishimura, Yamano, and Sasaoka (2005) Attainment Patterns in Uganda before and after UPE Female: Pre-UPE (age 20-24) Post-UPE (age 15-19) Male: Pre-UPE (age 20-24) Post-UPE (age 15-19)

Source: Nishimura, Yamano, and Sasaoka (2005) Attainment Patterns in Uganda before and after UPE Female: Pre-UPE (age 20-24) Post-UPE (age 15-19) Solid lines: Least poor Dashed lines: Poor Male: Pre-UPE (age 20-24) Post-UPE (age 15-19) Solid lines: Least poor Dashed lines: Poor

Attainment Profile: Suggestions Different attainment profile suggests different policy implications. –If enrollment rates are low, then policies should intend to improve enrollments through increased number of schools. –If drop-out rates are high, then efforts should be made to keep students in school. School quality matters!

Is Education System Efficient? Source: Pritchett (2004) Education Outcome Production Frontier ● If the education system is efficient, then the budget should be spent for expansion ● Gains in efficiency given the same budget Expansion of inefficient education system Budget is spend to increase efficiency

Policy Actions Supply Demand Physical expansion Expansion in spending Expansion in specific interventions Vouchers Gender Conditional transfer School lunch Late enrollment School fees Policy reform (returns would be higher) Source: Pritchett (2004)

Program Evaluations: More complicated than you think

Schooling as a Self-Selection Mechanism High Ability People Low Ability People Earnings Assume that they attend secondary schools and gain no skills Age Secondary graduates Primary graduates Earnings It appears that the secondary schooling increased the Earnings, but this is actually because of the self-selection. They do not attend secondary schools

Instead we need to control for people’s ability… High Ability People Earnings Assume that some of them attend secondary schools but some do not, and that the secondary schooling has some impacts on earnings. Age Secondary graduates Primary graduates Earnings The causal effect of secondary schooling is much smaller than the association (previous slide), which is upward biased because of the self-selection.

Another example: Endogenous Program Placement High Ability People Low Ability People Earnings Assume that the government provides training programs to them Age Others Programs graduates Earnings It appears that the program graduates have lower earnings than non-graduates and that the programs have negative Impacts. << A Reverse Causality They do not attend programs

The reverse causality was observed because … The reverse causality was observed because the programs were targeted to low ability people. The government programs are strategically placed (called endogenous program placement). Earnings

Econometrically these problems in evaluations are part of d “the omitted variables problem” or more generally “the endogeneity problem.” The problem is created by the correlation between independent variables and the error term: In econometrics … Where i indicates an observation (e.g., individual), y is an outcome variable, X is observed individual characteristics, Z represents the program participation, α is unobserved individual characteristics, such as ability, and e is the error term. If Z is correlated with α, which is part of the error term, then the correlation between Z and α causes biases. The direction of the bias on the coefficient of Z depends on the correlation between Z and αand between Y and α.

In general, the direction of the bias is determined by the signs of correlations between the dependent variable (y) and the omitted variable (α) and between the independent variable ( Ｚ ) and the omitted variable (α). Corr 1 Corr 2 Bias Positive Positive Over-estimate Positive Negative Under-estimate Negative Positive Under-estimate Negative Negative Over-estimate Corr 1 Corr 2

How can we overcome the endogeneity problems? Use instrumental variables that are correlated with the independent variables (endogenous variables). Use the Difference-in-Differences model: compare changes in an outcome before and after the participation in a program between programs participants and non-participants. Use the Fixed Effects Model: use panel data to eliminate fixed characteristics, such as ability. Use Randomized Experiments: place programs randomly instead of strategically.

The Difference in Differences Estimation The difference-in-differences estimator is δ= ΔY T - ΔY C This measures the net impact of the program participation. Earnings ΔY C : Control Group Non-Participants ΔY T : Treatment Group Participants

Evaluations through Experiments

The Primary School De-worming Project (PSDP) in Busia District, Kenya –75 primary schools (over 30,000 pupils) divided into three –25 Group A schools received free de-worming treatment in both 1998 and 1999 –25 Group B schools received free de-worming treatment in 1999 –25 Group C schools received free de-worming treatment in 2001

Source: Miguel and Kremer (2004) Econometrica

Evaluations through Experiments PROGRESA in Mexico: –Out of 495 localities, 314 localities were randomly selected for PROGRESA –In the selected 314 localities, about two-third of households were found “poor” by the previous census and eligible for educational grants. –Grants were provided to eligible children who were in grades 3 through 9 grades in elementary school and the next 3 years of junior school. T. Paul Schultz (2004). “School subsidies for the poor: evaluating the Mexican Progress poverty program,” Journal of Development Economics.

Source: Shultz (2004) Journal of Development Economics Poor Households Non-Poor Households PROGRESSA Localities S 1t S 3t Non-PROGRESSA Localities S 2t S 4t Hypothesis 1:D1 = (S1t – S2t) >0 Post program period Hypothesis 2:D1 = (S1t – S2t) =0 Pre-program period Hypothesis 3: DD1 = D1(Post) – D1(Pre) > 0

Source: Shultz (2004) Journal of Development Economics