SSL-CCT: Overview on Targeting and Beneficiaries Selection

Targeting and Beneficiary Selection

Romulo Paes de Sousa,

IDB-ADB South-South learning event on Conditional Cash Transfers

Manila, 16-19 April 2013

Why targeting?

• Maximize results in a context of scarce budget

• Political economy: voters are less resistant to fund selective interventions, especially those that include co-responsibilities

Methods

• Target group: poor, vulnerable, malnourished

• Unit of reference: household, family, individual

• Selection method:

–Geographical

–Categorical

–Means testing

–Community targeting/validation

Country Program Types of targeting Tool Geogra-

phic Categorical Means testing Commu-

nity Single

registry Brazil Bolsa

Família Income test

PETI

Working children under 16 years old

Income test

Colombia Más Familias in Acciόn

Displaced and

indigenous families

Quality of life index

Honduras PRAF/ Bono 10,000

PMT

Jamaica PATH PMT Mexico Oportuni-

dades

PMT Disconti-nued

Peru Juntos PMT

All countries in LAC combine different types of targeting mechanisms

Geographic targeting

• Widely used as the first stage of the process of selection of beneficiaries

• Attempts to identify areas (e.g. districts, municipalities, parishes, villages) with high incidence of poverty, malnutrition or vulnerability

• Requires data from population censuses and nationally representative household surveys

• Low cost, works well to identify areas with homogeneous socioeconomic characteristics

• Example: in Mexico, PROGRESA/Oportunidades rolled out by locality, based on the value of a marginality index

Categorical targeting

• Used to restrict access to the program to households with certain demographic characteristics, which are related to the human capital development objectives (program co-responsibilities)

• Brazil’s program of eradication of child labor (PETI, from its Portuguese acronym) targets households with working individuals under 16 years old

• More generally, CCT programs tend to focus on households with pregnant or lactating women, children and school-age youth.

• In some cases, they also include the elderly, individuals with disabilities and other selected groups

Means testing

• Used to further refine the selection of poor households, among those that satisfy geographical and categorical criteria

• In many countries, a proxy means score (a combination of assets and demographic characteristics) is compared with a predetermined eligibility threshold

• Proxy means test (PMT), one term hides heterogeneity of application. E.g.:

– For a long time Mexico defined a poverty score based on discriminant analysis (before adopting a regression model)

– Colombia’s Familias en Acciόn has recently developed the new Colombian Multidimensional Poverty Index (CMPI)

Colombian Multidimensional Poverty Index – selected dimensions

20

2.3.3 Weighting structure

There is no definitive procedure of assigning weights over dimensions in a

multidimensional measure of poverty. For the Colombian Multidimensional Poverty

Index we use a nested weighting structure where each dimension has the same weight

(0.2) and each variable has the same weight within each dimension.46

This weighting

structure was established based on the following points: i) although the weighting

structure should ideally take into account correlations between variables, there is still no

well-established way to implement this without compromising some of the indicator’s

other properties47

ii) the equal weight assigned to each dimension reflects their equal

importance as constituents of quality of life, and iii) in the debate among experts this

was the option on which there was greater agreement.

Table 2. Dimensions and Variables for CMPI

Dimension Variable

Cutoff point Variable Indicator

Household

education

conditions

(0.2)

Educational achievement

(0.1)

Average education level for people 15 and older living in a

household 9 years

Literacy (0.1) Percentage of people living in a household 15 and older who

know how to read and write 100%

Childhood

and youth

conditions

(0.2)

School attendance (0.05) Percentage of children between the ages of 6 and 16 in the

household that attend school 100%

No school lag (0.05)

Percentage of children and youths (7–17 years old) within

the household that are not suffering from school lag

(according to the national norm)

100%

Access to childcare

services (0.05)

Percentage of children between the ages of 0 and 5 in the

household who simultaneously have access to health,

nutrition and education

100%

Children not working

(0.05)

Percentage of children between 12 and 17 years old in the

household that are not working 100%

Employment

(0.2)

No one in long-term

unemployment (0.1)

Percentage of a household´s EAP that is not facing long-

term unemployment (more than 12 months) 100%

Formal employment (0.1) Percentage of a household´s EAP that is employed and

affiliated with a pension fund (formality proxy) 100%

Health

(0.2)

Health insurance (0.1) Percentage of household members over the age of 5 that are

insured by the Social Security Health System 100%

Access to health services

(0.1)

Percentage of people within the household that has access to

a health institution in case of need 100%

46 The weight assigned to each dimension and variable is shown in parenthesis. 47 “Nor has i

t

even been established that the potential interrelationships must be reflected in an overarching

methodology for evaluating multidimensional poverty. Instead, the interconnections might be the subject of separate

empirical investigations th at supplement, but are not necessarily part of, poverty measurement” (A lkire and Foster

2007).

Means testing

• A famous alternative to PMTs. In Brazil, declared per-capita income is compared with a poverty line

• In order to avoid inclusion errors due to income misrepresentation, the Brazilian authorities have:

– Elaborated a comprehensive monitoring system which relies on a modern unified registry of beneficiaries

– Leveraged the capacity of local governments to keep the list of beneficiaries clean and up to date

Means testing

PMT

• Identifies chronic poor

• Requires larger initial investment, but less verification and recertification efforts

Income test

• Identifies chronic and transient poor

• Requires stronger audits and monitoring, and higher local government capacity

Community validation

• Exploits personal knowledge on the living standards of the applicants, in order to correct errors of either inclusion or exclusion

• Juntos, Peru: – Served to filter out approximately 10% of the households

initially selected

– Yet, communities were often reluctant to indicate who should be excluded from the program (due to community relations)

• Oportunidades, Mexico: – Initially used community validation in rural areas. This phase,

however, was later discontinued

Targeting errors: inclusion and exclusion

Source: Lindert et al. (2007) The Nuts and Bolts of Brazil’s Bolsa Familia Program: Implementing Conditional Cash Transfers in a Decentralized Context The World Bank.


Coverage and leakage in Peru’s Juntos, relative to other programs Source: Stampini (2012). Notes: national poverty line, compared to consumption

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Beneficiarios (nivel individual)

Pobre extremo Pobre (no extremo) Non pobre

0% 10% 20% 30% 40% 50%

Juntos

Papilla (PACFO)

Canasta Familiar

Almuerzo Escolar

Vaso de Leche

Desayuno Escolar

Comedor Popular

% de hogares que reciben el programa, por condición de pobreza

CCTs are generally better targeted than other social assistance programs

Country Program Year

% of individuals <2.5$ that receive the

program

% of beneficiari

es with income >$2.5$

% of individuals

<4$ that receive the

program

% of beneficiari

es with income

>$4$ Brazil Bolsa Familia 2009 55.1 50.0 46.8 28.1 Colombia Familias en Acc. 2010 53.4 71.4 49.9 49.4 Jamaica PATH 2010 56.0 77.9 50.5 40.5 Mexico Oportunidades 2010 53.4 61.4 42.5 35.8 Peru Juntos 2010 37.4 33.1 28.5 11.3

Coverage and leakage in selected CCT programs in LAC

Source: Stampini and Tornarolli (2012)


Hence the importance to invest and dynamically manage the registries of beneficiaries

Yet, under-coverage and leakage persist

Using the single registry for targeting improvements: the Brazilian example

Objective • Identify population eligible for the Bolsa Família

and other programs. It comprises 10 sets of questions

Target population for the single registry • Families with monthly per capita income <= ½

minimum wage, or monthly total income <= 3 minimum wages

• With specific strategies for minorities and ultra-poor populations

• In December 2012, circa 24.9 million families were registered


Methods of classification

• Person of reference declares the income of all members of the family, in an interview on a premise of a public building

• Government stimulates the performance of the interview at the household

Strategies for preventing and correcting underreporting of family income

• Geographical targeting

• Periodical re-certification every 2 years


Index of Decentralized Management – IDM. • Incentive to the local managers to improve the reliability

of the Registry. The municipality can receive up to US$ 1.66 per record if: the local Registry is consistent with demographic estimates, the data is updated, and data related to education and health conditionalities are present. The resources can be allocated for improving technological capacity of the administration and for funding programs associated with the Bolsa Familia.

Incentives to the families. • Families that spontaneously inform that earning have

grown above the threshold can retain the bank card, which accelerates the reintroduction of the benefit in case of returning to a vulnerable condition.


Auditing • Electronic validation of the data. Data matching with

other public registries, such as: national formal employment and wages registry, death registry, and retirement and pensions registries.

• Visits to the municipalities by own staff or in collaboration with other agencies

• In 2009, inconsistencies were identified in 878,026 records of beneficiaries and 1,467,932 records of non-beneficiary families. After the fieldwork, 194,869 families were expelled from the program, and among the non-beneficiaries: 629,692 families updated their records.

Other Country examples

• Jamaica, the Beneficiary Identification System (BIS) encompasses home visits, recertification, database crosschecks, and the establishment of appeals committees to reassess the situation of households at the margin of eligibility

• In Colombia, the success of Familias en Acción is partially based on the credibility that the SISBEN has on the beneficiaries and non beneficiaries. The SISBEN is recognized as a competent system for selecting poor families in a transparent manner

Challenges

• Urban and rural areas require different types of operation – Lack of ID

– Cultural differences (language, time reference, concept of family, etc.)

– Access to the services

• Hard to reach/hidden populations

• Integrating social protection data with health and education data

• Qualified staff in sub-national levels

• The roles of the sub-national levels

The lessons

• Mixed targeting approaches are necessary to improve the targeting

• PMT helps to select the beneficiaries in the first stage

• Good targeting adds credibility to the CCT programmes

• Single registries are key tools in the operation of the programs (targeting, monitoring, payment, re-certification, etc.)

Thank you

Questions and comments:

[email protected]

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SSL-CCT: Overview on Targeting and Beneficiaries Selection

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