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Targeting and Calibrating Educational Grants for Greater Efficiency

Targeting and Calibrating Educational Grants for Greater Efficiency Elisabeth Sadoulet and Alain de Janvry University of California at Berkeley. Lessons for an APA Client : Government of Mexico (SEDESOL) and World Bank-Mexico.

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Targeting and Calibrating Educational Grants for Greater Efficiency

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  1. Targeting and Calibrating Educational Grants • for Greater Efficiency • Elisabeth Sadoulet and Alain de Janvry • University of California at Berkeley

  2. Lessons for an APA Client: Government of Mexico (SEDESOL) and World Bank-Mexico. Question: Assess the efficiency of Progresa/Oportunidades as part of the Contigo anti-poverty/social development strategy. Could the same result be achieved at lower cost? Could more be done with the same budget? APA outline: Discuss the question posed by the client. Understand Progresa and identify sources of information. Formulate a hypothesis and a research strategy Assess how effective the current approach is. Specify alternative approaches. Discuss pros and cons of the alternatives proposed. Conclude: make a recommendation.

  3. I. Conditional cash transfers programs for education • Typical approach (Progresa, PRAF, FISE, Bolsa Escola): • Target on poverty (poor mothers in poor rural communities). • Make uniform transfers (by grade and gender). • Question: How much budget saving and efficiency gain could be achieved if these programs were redesigned for maximum efficiency? • Efficiency (maximum enrollment for given budget) is shown to be increased by: • Targeting onrisk of not going to school. • Making transfers calibrated on that risk. • Objective of this paper: Use the educational component of Progresa to: • Calculate the magnitude of the budget saving and efficiency gains from this efficient scheme. • Identify rules to make the approach operational.

  4. Note • Progresa had other objectives than educational achievements, in particular poverty reduction. • Progresa had integrated components of nutrition/health and education. • Progresa was a pioneer in conditional transfer schemes. We disregard these aspects and hence, this is not an evaluation of Progresa. But we use Progresa’s exceptional data base for thinking about the design of a pure educational program.

  5. Conclusion • Targeting and calibrating on risk of non-enrollment would: • Save 55% of the educational budget ($230million). • Increase efficiency by 53 to 72% with remaining budget. • Questions • Is targeting and calibrating transfers on risk of non-enrollment feasible? • Is it precise? No less than poverty. • Could self-targeting be feasible? Yes with community supervision.

  6. 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 ($2.2 billion in 2003) • 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.

  7. 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).

  8. 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.

  9. 3. Sample • From the Evaluation study: 506 communities; 24,000 households. • Randomization: 320 treatment communities, 186 control communities (3 years phasing-in of program). • Treatment: 11,000 children eligible for educational transfers. • Our study on continuation rate rather than enrollment rate. • Only children still in school in 1997: • - 9,500 eligible for educational transfers • - In both treatment and control villages: 3,519 finish primary school = population analyzed.

  10. III. How effective is the current targeting on poverty? 1. Payments to poor households for primary school enrollment are unnecessary • School continuation rates in sample villages

  11. Percentage of children graduating from primary school: 91%. • Enrollment gains from Progresa transfers about 1% point/grade. • Conclude • Can save 55% of educational budget 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.

  12. How effective for entry into secondary school? • Double difference impact of Progresa = 11.6% points. Increases enrollment of poor: from 63.6% to 76.6%. • Progresa fully erases the educational disadvantage of the poor relative to the non-poor.

  13. IV. Predictive model of enrollment 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)

  14. Results • Average effect: 11.1% Enrollment Without program With program Poor 65.1 76.0 Non-poor 73.8 – All 68.2 75.2

  15. 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.

  16. Visualizing the effect of transfers

  17. V. Transfer scheme for maximum efficiency • With uniform transfers • Optimal scheme Targeting: on kids most at risk (up to probability of enrolling of 61.7%) Transfer amount: 343 pesos/month (compared to 200-210 pesos/month in Progresa) •  ResultsRaises the enrollment rate in the population from 75.2% (targeting on poverty) to 79.8%.

  18. With calibrated transfers • Optimal scheme: • Targeting and amount of transfer are both determined by the probability of enrolling. • Amount: Adjust the levels of transfer so that the cost of the marginal child brought to school is equal across groups. • Targeting: Threshold established for average cost per enrolled child = cost of marginal enrolled child

  19. Results: • Transfers given to all children with probability to enroll lower than 64%. Transfers vary from 500 pesos/month (for the children with lower probability) to 220 pesos/month (for the children less at risk), averaging 341 pesos/month. • Raises the enrollment rate in population from 79.8% (targeting on non-enrollment risk with uniform transfers) to 80.3%.

  20. Figure 4. Total direct and leakage costs under (1) the poverty targeted and (2) the efficient variable transfers schemes

  21. VI. 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). • Structural features: reflecting fundamental risk, not conjunctural conditions. Not a safety net. (e.g., should not have employment)

  22. 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

  23. VII. Discussion: 1. Restricting transfers to the poor? 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.

  24. Similar program for the poor only? Objective: Conditional transfer to the poor to compensate for …. (risk, credit failure, etc.) whatever prevent them from going to school because of their poverty. With targeting on risk, calibrated transfers, and feasible Target 61.2% of the poor most at risk Overall enrollment of poor increases from 65.1% to 80.6%, rather than 76% with Progresa

  25. 2. Is it feasible to target on risk instead of poverty? Is it more difficult to predict risk than poverty?No When poverty not “measured” but predicted by a welfare index Individual probabilities predicted with huge standard error By groups, very similar results to prediction by welfare index

  26. Is self-targeting feasible? Yes Need that: • The rules be publicly known. • Potential beneficiaries self-declare and “prove” their eligibility. • Community supervision to deter cheating: Private information locally public (e.g., educational level of parents). • As opposed to eligibility based on means tests or proxies that can be manipulated.

  27. VIII. Conclusions • Conclusion 1 • 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 53%. • Hence, it is worth doing!

  28. Conclusion 2: Progresa transfers in the population at large vary little by expenditure level. Equivalent to P1 targeting! Hence, households’ educational needs are not a good targeting device for poverty reduction.

  29. Conclusion 3: How to manage an antipoverty strategy like Contigo? Ultimate objective of strategy = poverty reduction. Intermediate objective of each program = increase the educational/health/nutritional achievement of the population to decrease future poverty. Approach: 1. Manage each program for maximum efficiency in achieving its intermediate objective (education, health, nutrition). Target on poverty only if poverty is the best correlate of risk of not meeting the objective. Otherwise, target on risk with a more complete predictor index. Combine programs (Contigo, PRSP) in an overall poverty reduction strategy. Warning: do not try to achieve the poverty reduction objective through targeting each program on poverty.

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