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

Elisabeth Sadoulet and Alain de JanvryUniversity of California at Berkeley

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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.

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NoteProgresa 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.

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II. Scoreboard on Progresa

1. The programStarted 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.

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2. 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 feedbacksCorrections by community to list of predicted poor made by Progresa (marginal changes only).

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3. 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.

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4. 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.

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III. Is poverty targeting efficient to increase educational achievements?

1. Payments to poor households for primary school enrollment are unnecessary

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P1 P2 P3 P4 P5 P6 S1 S2 S3 S4

Continuation rate (%)

Primary school

Lower secondary school

Secondary 163.6%

Grade

PROGRESA INTERVENTION

Upper secondary

school

43.3%

School continuation rates without PROGRESA intervention

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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.

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2. 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?Table 2. Double difference estimation of the impact of PROGRESA on continuation in secondary school

Number of In Progresa In control Differenceobservations villages villages

(3,519) (%) (%) (% points)

Poor 2,242 76.6 63.6 13.0 (1.9)Non-poor 1,277 74.2 72.9 1.4 (2.5)

Difference 2.4 -9.2 11.6(% points) (2.0) (2.5) (3.2)Standard errors on differences in parentheses.

Enrollment rate

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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))

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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%

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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% attendNon-poor

26% do not attend: transfer needed.

ConcludeTargeting 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.

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IV. Targeting on risk of not going to school

1. With uniform transfersSimulation procedure: Exhaust the current budget starting with children most at risk.ResultsRaises the enrollment rate in the population from 75.2% (targeting on poverty) to 77.2%.

Table 5. Comparing poverty and risk of low enrollment

Percent of observations Non-poor Poor Total

Not at risk 15.1 16.3 31.3

At risk 21.2 47.4 68.7

Total 36.3 63.7 100.0

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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%

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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%.

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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%

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Targeting on poverty Targeting on risk with uniform transfer

Targeting on risk with calibrated transfers

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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).

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Selection criteria usedChild 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

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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.

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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%

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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%

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VI. Discussion: Is it feasible to target on risk instead of poverty?

1. Is it more difficult to predict risk than poverty? No

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Deciles of Predicted Probability

Not enrolled

Observed

ObservedPredicted

Poor

Predicted

se = 2.6%

se = 1.8%

2. Is self-targeting feasible? YesAll variables can be self-declared.Community supervision: Private information locally public.

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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.

VII. Conclusion