BENEFIT INCIDENCE ANALYSIS FOR PUBLIC CHILD HEALTH AND NUTRITION

PROGRAMS IN PERU

Martín Valdivia

Group of Analysis for Development

February 2004

Description of the study

Analyze the targeting performance of 3 of the largest public food transfer programs for children in Peru School Breakfast (SB) Vaso de Leche (VL) - Glass of Milk Aggregate of food transfer programs for children under 3

(PANFAR, PACFO, PAI, wawa-wasi/PRONOEI)

Uses individual data instead of household data Allows to identify leakages within the household when specific

age groups are identified and consumption occurs at home

Test the robustness of targeting measures (undercoverage, leakage) to changes in: The specific poverty line assumed and The size and scale of the program

Summary of analyzed programs

Size of analyzed programs

These programs concentrated over the 80% of public resources allocated to food programs by the year 2000

Total Budget for Food Programs in Peru (thousands of nuevos soles):

1998 1999 2000

Vaso de Leche (VL) 285,124 305,124 325,124

School Breakfast (SB) 198,598 248,588 237,094

Child Oriented Food Programs (CHOFP) 111,906 187,493 121,009

Sub-total 595,628 741,205 683,227

Total budget food programs 684,931 902,347 838,569

Source: 1998-1999, STPAN (1999). 2000, Instituto Cuánto (2001).

Coverage of social programs by quintile: household vs individual samples

Analysis at the household level suggests a much larger pro-poor bias for all programs

In both cases, the CHOFP aggregate shows the largest pro poor bias

Coverage of social programs, by quintiles of the socioeconomic variable 1/*

I II III IV V Total

Individual level

School Breakfast (SB) (a) 54.2 55.3 42.2 38.7 30.1 44.1

Vaso de Leche (VL) (b) 22.1 23.3 25.1 20.4 13.8 20.9

Child Oriented Food Programs (CHOFP) (c)(d) 18.9 16.0 13.9 4.7 3.4 11.4

School Insurance (SI) (e) 37.8 38.3 43.6 45.9 42.9 41.7

Household level

School Breakfast (SB) (a) 62.0 49.2 44.5 29.9 14.3 40.0

Vaso de Leche (VL) (b) 45.9 42.2 34.4 28.0 15.7 33.2

Child Oriented Food Programs (CHOFP) (c)(d) 25.5 17.7 13.0 5.8 3.6 13.1

School Insurance (SI) (e) 48.5 50.0 48.0 40.2 21.7 41.7

Source: 2000 LSMS

(a) As a percentage of (households with at least one) children between 4 and 13 years old who are actually attending to a public school. (b) As a percentage of (households with at least one) children under 13 years old and women who are actually pregnant or breast feeding. (c) Includes PANFAR, PACFO, PAI, Wawa Wasi / PRONOEI / Cuna. (d) As a percentage of (households with at least one) children under 3. (e) As a percentage of (households with at least one) children between 4 and 18 years old who are actually attending to a public school.

Targeting errors: Leakage and undercoverageEstimated Leakage and Undercoverage Rates for Each Program

Leakage (1) Undercoverage (2)

Global Urban Rural Global Urban Rural

LSMS 2000

SB (a) 36.8 39.1 35.4 52.6 66.6 38.1

VL (b) 45.2 51.7 38.6 77.2 80.1 74.14

CHOFP (c)(d) 34.9 43.2 32.8 85.7 94.8 76.8

SI (e) 37.6 39.9 34.4 59.2 57.2 61.5 (1) As a percentage of the individuals within the restrictions of each program, who were poor. (2) As a percentage of the individuals within the restrictions of each program, who benefited.

All other notes as table above.

Undercoverage: proportion of program’s target population that does not receive a

transfer. Leakage: Proportion of program beneficiaries that are not part of the target population

VL program stands as the program with worst leakage, especially in urban areas There does not seem to be any systematic relation between the size of the program and

their targeting.

Leakage within the household

Target population is defined in 3 dimensions: age school affiliation socio-economic status

A leakage occurs not only when the beneficiary is from a non-poor household, but also because he/she is not from the target age group

We can check the importance of each restriction by re-estimating the leakage rate while removing one restriction at a time

Leakage rates under alternative set of restrictions

All

restictions No age

restriction No school restriction

Only poverty restriction

SB 36.8 31.9 35.8 28.5

VL 45.2 32.3 45.2 32.3

CHOFP 34.9 18.1 34.9 18.1

SI 37.6 36.5 36.9 35.8

Source: 2000 LSMS

The age restriction is significantly much larger for the VL program and the

CHOFP aggregate, which are programs that allow for consumption within the household.

For VL program, 1 out of 4 leaks are poor but out of the age rangeFor the CHOFP aggregate, almost 1 out of 2Result is consistent with findings in Alcazar et.al. (2003) using PETS, for VL

The school restriction is not significant for neither the SB nor the SI program.

Targeting errors and the poverty line

Programs focus on the neediest and most vulnerable, but Poverty status is not unarbitrarily determined. For the purpose of

the study, we use expenditure information and a poverty line, which is always arbitrarily determined

More importantly, program officials do not use household surveys to identify the socio-economic level of the potential beneficiaries

limited to geographic targeting instruments even with individual targeting instruments, they base their decision on

some observable characteristics to determine the poverty level (proxy-means tests)

Consequently, it is important to check if estimates of targeting performance change significantly as we move the poverty line

Comparing social programs across the income distribution

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Figure compares the concentration curves (CCs) for the four programs

A CC plots the proportion of beneficiaries to the left of any point of the income distribution

The CC of the CHOFP aggregate clearly dominates those of the rest of the programs,

it can be said that CHOFP has the largest pro-poor bias, regardless of the level of the poverty line.

No significant difference appears between the VL and SB programs

The proportion of poor and non-poor that benefit of a program at a given time may not be a good indicator of how an expansion, or contraction, would affect the poor.

Positive participation costs can imply early or late capture of a program by the non-poor, if they differ for the poor and non-poor, and change with the scale of the program. The higher costs related to reaching more remote areas is often

raised as the typical argument in favor of early capture.

Late capture could result from the fact that small pilot projects are more carefully monitored and face less political pressures.

Incidence analysis at the margin

We can follow Younger (2002) and use the heterogeneity across regions and over time to estimate the impact of a program expansion or contraction for the poor. The idea is to estimate the following equation:

The dependent variable is the program participation rate for quantile q in a given domain in a particular year. The explanatory variable is the program participation rate for the specific year.

can be interpreted as the marginal effect of an increase in program participation at the national level on the participation rate in a particular quantile

would indicate that a general expansion (contraction) in coverage will cause a more than proportional increase (reduction) in participation for that quantile

Incidence analysis at the margin II

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We can estimate the so that they sum to five, and plot a marginal CC for each program

Then, we can compare the marginal CCs with the average ones

Disregarding the age restriction, both programs appear to be more pro-poor at the margin than on average

These results suggest that, even though both programs show a mediocre targeting performance on the average, they seem to have a more pro poor behavior on the margin.

This means that cutting (expanding) them would (benefit) the poorest more than proportionately

Marginal Incidence Analysis (MIA) for the SB and VL programs

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Marginal incidence analysis Average incidence analysis

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Summary of results and implications

First, the age restriction is found to be very important for VL and CHOFP programs, which are the ones that allow for consumption within the household,

The VL program stops being the one with the worst targeting performance and the CHOFP aggregate becomes by far the program with lowest leakage. (34%)

This result suggests that food programs that allow for consumption of the ration in the household are not able to impose their preferences on the distribution of the transfer across household members, regardless of the nature of the ration.

Lack of consideration of these intra-household arrangements reduce the size of the transfer per capita and limit the possibility for them to have a nutritional impact on the target population. (consistent with Stifel and Alderman, 2003)

Summary of results and implications II

Second, I find that the SB and VL programs has a very pro-poor behavior at the margin despite having a very mediocre targeting performance on average (early capture)

It suggests the need to be cautious about making decisions based on the average targeting performance of programs. They could show large leakages on the average, but still a cut (expansion) could damage (benefit) the poor more than proportionately

One usual explanation is that programs start in more urban areas that are easier and less costly to reach

Summary of results and implications III

Nevertheless, it is important to look at delivery mechanisms organized for these programs In both cases, the central government assigns resources by district based

on poverty map. Then, municipalities identify localities on a need basis. Within localities, mother´s clubs define actual beneficiaries

In practice, any mother´s club that gets to be registered will never be retired as a beneficiary. Some with registered households

Anecdotal evidence suggest the following: new entrants tend to be from very poor localities but, as time passes, their economic situation improves but they cannot be taken out of the registry

It is important to notice that any of these explanations take the priority away from improving proxy-means test instruments. The latter focus on the political economy behind delivery mechanisms

Group of Analysis for Development

www.grade.org.pe