Targeting and Calibrating Educational Grants: Focus on Poverty or on Risk of Non-Enrollment?

1. Targeting and Calibrating Educational Grants: • Focus on Poverty or on Risk of Non-Enrollment? • Elisabeth Sadoulet and Alain de Janvry • University of California at Berkeley

2. I. Conditional cash transfers programs for education • Typical approach (Progresa, PRAF, FISE, Bolsa Escola): • Target on poverty. • Make uniform transfers (e.g., by grade and gender). • Question: How much budget saving and efficiency gain could be achieved if these programs were redesigned to: • Target on risk of not going to school? • Make transfers calibrated to the needed incentive to participate? • Objective of this paper: Use the educational component of Progresa to: • Calculate the magnitude of the budget saving and efficiency gains from targeting on risk instead of poverty. • Identify rules to make the approach operational.

3. Note • Progresa has other objectives than educational achievements, in particular poverty reduction. Hence, not an evaluation of Progresa. • Conclude • Targeting on risk of non-enrollment instead of poverty would: • Save 55% of educational budget. • Increase efficiency up to 100% with remaining budget. • Questions • Is targeting on risk of non-enrollment feasible? • Is it precise? No less than poverty. • Could self-targeting be feasible? Yes with community supervision.

4. II. Scoreboard on Progresa • 1. The program • Started in 1997. • Cash transfers to mothers in selected households for education, health, and nutrition. • Overall budget: $950 million in 2000 ($1.8 billion in 2002) • Benefits 2.6 million families. • Educational component: Educational grants for children from 3rd year of primary to 3rd year of secondary conditional on school attendance. • $418 million/year. • Benefits 2.4 million children: 1.6 million in primary school, 800,000 in secondary. • Average educational transfer per child: $175/year.

5. Targeting procedure: Three steps • Step 1: Geographical targeting Rural community marginality index based on indicators from population census. • Step 2: Household targeting Predicted welfare index (confidential formula) based on information from benchmark census in targeted marginal communities. • Step 3: Community feedbacks Corrections by community to list of predicted poor made by Progresa (marginal changes only).

6. Determinants of level of cash transfer • Payment per child that qualifies (6–18 years old, eligible grade) uniform, except for adjustment by: Grade level (from $7/mo. 3d primary, to $27/mo. girls 3d secondary). • Gender (higher for girls in secondary: 5%, 12%, 16% higher by grade) • Cap to school payments (affects 13.4% of eligible children, saves 17% of educational budget). • Note: Caps is what allows to measure the impact of variable amounts transferred on school attendance decision of beneficiaries.

7. Design of impact study • 506 communities, 24,000 households. • Randomization: 320 treatment communities, 186 control communities. • Panel data: benchmark census and follow-up survey every 6 months for 3 years. • Treatment: 11,000 children eligible for educational transfers, 9,500 of them in school in 1997. • In both treatment and control villages: 3,519 finish primary school = population analyzed.

8. III. Is poverty targeting efficient to increase educational achievements? 1. Payments to poor households for primary school enrollment are unnecessary • School continuation rates without PROGRESA intervention

9. Percentage of children graduating from primary school: 91%. • Enrollment gains from Progresa transfers about 1% point/grade. • Conclude • Can save 55.4% of educational budget or $230 million/year by not making transfers for primary school. • Better use special fellowship programs for the few children at risk in primary. • Critical decision requiring cash transfer is entering in secondary school.

10. How effective is the current targeting on poverty? • (Entry into secondary school) • Double difference impact of Progresa = 11.6% points. • Increase in enrollment of poor: from 65% (counterfactual) to 76.6%. • Progresa fully erases the educational disadvantage of the poor relative to the non-poor. Could it be more effective?

11. III. Behavioral model of enrollment decision in secondary school Pr(enrollment in secondary) = function of: + Boy - Age + Father literate + Highest level of educational achievement in the household + Mother indigenous - Number of working adults in the household (esp. if self- employed) - Household is categorized by Progresa as poor - Household has poor dwelling characteristics + Total expenditure level - Distance to school + Progresa transfer (dummy or amount))

12. Summary on enrollment rates in the whole population • (poor and non-poor) • Predicted secondary enrollment rates in population: • Without program (Mexico’s marginal communities) 68.2% • With targeting on poverty and uniform transfers (Progresa) 75.2%

13. Why targeting on poverty is not maximally efficient for school achievement? 65% would have attended w/o transfer (86% of budget wasted) 76% attend Poor 11% attended because of transfers (target) 24% do not attend: transfer offered insufficient 74% attend Non-poor 26% do not attend: transfer needed. Conclude Targeting transfers on poverty is inefficient since: 65% of subsidized poor do not need subsidy. 24% of poor would need larger subsidy. 26% of non-poor need subsidy.

14. IV. Targeting on risk of not going to school 1. With uniform transfers Simulation procedure: Exhaust the current budget starting with children most at risk. ResultsRaises the enrollment rate in the population from 75.2% (targeting on poverty) to 77.2%.

15. Summary on enrollment rates in the whole population (poor and non-poor) Predicted secondary enrollment rates in population: Without program (Mexico’s marginal communities) 68.2% With targeting on poverty and uniform transfers (Progresa) 75.2% With targeting on risk and uniform transfers 77.2% Efficiency gain over Progresa (77.2 – 68.2) / (75.2 – 68.2)  29%

16. 2. With calibrated transfers • Simulation procedure: • Adjust the levels of transfer to the minimum needed to give incentive to send child to school. • Results: • Raises the enrollment rate in population from 77.2% (targeting on non-enrollment risk with uniform transfers) to 82.2%. • Conclude • Efficiency gain due to targeting on risk with calibrated transfers instead of poverty with uniform transfers = (82.2 – 68.2) / (75.2 – 68.2)  100%.

17. Summary on enrollment rates in the whole population (poor and non-poor) Predicted secondary enrollment rates in population: Without program (Mexico’s marginal communities) 68.2% With targeting on poverty and uniform transfers (Progresa) 75.2% With targeting on risk and uniform transfers 77.2% Efficiency gain over Progresa 29% With targeting on risk and calibrated transfers 82.2% Efficiency gain over Progresa 100%

18. Targeting on poverty Targeting on risk with uniform transfer Targeting on risk with calibrated transfers

19. V. How to make a cash transfer program targeted on risk implementable • Targeting criteria need to be: • Easy to observe: Exclude information on expenditure and poverty variables. • Non-manipuleable: Exclude age (parents could postpone sending child to school to cash more). • Simple to implement: Use discrete transfer categories (multiples of 50 pesos between 50 and 350).

20. Selection criteria used • Child characteristics • + Girl • – Rank among children • Parents characteristics • – Father’s and mothers literacy (Y/N) and education level • – Household’s maximum education • – Mother is indigenous • + Mother’s age • Demographic structure • + No of children 1-10 years old • + Number of children 11-19 years old • Employment structure • + Number of agricultural workers, self-employed, unpaid family workers. • Characteristics of house • + Persons/room in dwelling • – Dwelling has water • – Dwelling has television • Village characteristics • + No secondary school in village • + Distance to secondary school • State dummies

21. With targeting on risk, calibrated transfers, and feasible 80.6% Efficiency gain over Progresa 77% Who are the poor not at risk of not enrolling? (They will be excluded when targeting on risk) Have educated parents. Live near a secondary school. Who are the non-poor at risk of not enrolling? (They will be included when targeting on risk) Have uneducated parents. Live far away from a secondary school.

22. Similar program for the poor only? Objective: same enrollment as non-poor With targeting on risk, calibrated transfers, and feasible 73.8% Budgetary gain over Progresa 56.4%

23. Summary on enrollment rates in the whole population (poor and non-poor) Predicted secondary enrollment rates in population: Without program (Mexico’s marginal communities) 68.2% With targeting on poverty and uniform transfers (Progresa) 75.2% With targeting on risk and uniform transfers 77.2% Efficiency gain over Progresa 29% With targeting on risk and calibrated transfers 82.2% Efficiency gain over Progresa 100% With targeting on risk, calibrated transfers, and feasible 80.6% Efficiency gain over Progresa 77%

24. Discussion: Is it feasible to target on risk instead of poverty? • 1. Is it more difficult to predict risk than poverty?No 2. Is self-targeting feasible? Yes All variables can be self-declared. Community supervision: Private information locally public.

25. VII. Conclusion • Based on the Progresa experience, using a feasible program of targeting on risk of non-enrollment instead of poverty would: • Save 55% of the educational budget by not sponsoring primary education. • Increase the gain of enrollment in secondary education by 77%. • Hence, it is worth considering, especially in a context of pressing demands for greater efficiency in the use of domestic / foreign aid budgets.