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Social Indicators Research

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01.06.2013

Multidimensional Targeting: Identifying Beneficiaries of Conditional Cash Transfer Programs

verfasst von: Viviane Azevedo, Marcos Robles

Erschienen in: Social Indicators Research | Ausgabe 2/2013

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Abstract

Conditional cash transfer programs (CCTs) have two main objectives: reducing poverty and increasing the human capital of children. To reach these objectives, transfers are given to poor households conditioned on investments in their children’s education, health, and nutrition. Targeting mechanisms used by CCTs have been generally successful in identifying the income poor but have not fared as well in identifying households that under-invest in human capital. These mechanisms do not consider the multidimensional aspect of poverty, even when composite measures are used, as they do not capture each dimension-specific deprivation. This paper proposes a multidimensional targeting approach to identifying beneficiaries that explicitly takes into consideration the multiple objectives of CCTs and the multiple deprivations of the poor household. Results indicate that the proposed multidimensional targeting methodology significantly improves the selection of households with children who are most deprived in the dimensions often relevant to CCTs. In the case of Mexico’s Oportunidades, ex-ante evaluation results indicate that the multidimensional identification of beneficiaries increases the impact of transfers on school attendance compared to alternative targeting models.

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Rawling (2005) show the specific differences in CCTs programs for six Latin American countries.

Although these programs do not directly monitor school performance, evaluations show that performance is related to school attendance and the children’s health and nutrition (Parker et al. 2005; Todd et al. 2005).

For an exhaustive review and evaluation of targeting mechanisms see Coady et al. (2004b).

Significant variations also exist in how the implementation is done—whether households are visited; whether some variables are verified as part of the application process for all or for a sample of applicants; whether the staff members who help complete applications are permanent or contract workers and to which agency they report; and other such differences.

The target population of CCTs are income-poor households that under-invest in the human capital of their children (Fiszbein and Schady 2009).

For example, although Panama's CCT has reached a number of households similar to the size of the target population, two important groups of extreme poor are not beneficiaries of the Red de Oportunidades: 67 % of children aged 6–14 years old who do not attend school, and 48 % of the children aged 0–4 years old who have chronic malnutrition. These calculations are available upon request.

The value currently used by the program is 0.69. For more details on the targeting mechanism of PROGRESA/Oportunidades, see Orozco and Hubert (2005), Regalia and Robles (2006), Coady and Parker (2009), Azevedo (2010).

Until 2009, three official poverty lines were used to measure poverty in Mexico. Throughout this text, we use the capability poverty line (pobreza de capacidades) which is the poverty line used by the Oportunidades program during the period under consideration (CONEVAL 2007). Capability (monetary) poverty is defined as the inability to obtain a basic food basket and meet the necessary health and education expenses, even if the household were to use all its available income solely for these purposes (CONEVAL 2007). Since 2010, in order officially measure multidimensional poverty, CONEVAL has defined a minimum welfare line (which only covers the cost of the food basket) and an economic welfare line (which covers the food basket plus other goods and services).

Calculations are available upon request.

Alkire (2007) refer to the first property as “deprivation-focus” and to the second as “dimensional monotonicity”.

In the framework of these models, the urban–rural differences are captured through the characteristics of the places where the households reside, which play a relevant role for understanding individual behavior. The conceptual schemes for those models can be found in UNICEF (1998) and World Bank 2004.

UNICEF (1998Figure 5) shows an example of the complexity of considering the diverse causes of child malnutrition, assessing nutritional status, and identifying the most appropriate mix of actions.

In the education dimension, the mother's schooling and the other school aged members' education are considered to influence the school performance of a child directly. For this reason, according to cited literature, the other household members' education was not considered.

On this issue, the alternatives cited by Alkire and Foster (2011) for multidimensional poverty measurement include statistical, survey-based, normative-participatory, or frequency-based, or a combination of these.

The proposal and results are consistent with the suggestion made by Alkire and Foster (2011) that repeated applications and reasonable evaluations can lead to a range of plausible values for “minimum deprivations” and a single value can then be selected for the main analysis and alternative values to check its robustness.

According to (CONEVAL 2010) 38.8 % of the population is multidimensionally poor (i.e., per capita household income is below the economic welfare line) and deprived in at least one out of six social deprivations. CONEVAL also has estimated that 5.8 % of the population is extreme multidimensionally poor (i.e., per capita household income is below the minimum welfare line) and deprived in at least three social deprivations (www.coneval.gob.mx). Since late 2009, Mexico officially measured poverty using a multidimensional approach.

Due to the limited thematic coverage of the data sources used, we restrict the analysis to these two indicators, but ideally we should use indicators that account for outcome or impact indicators of CCT programs, such as compliance with vaccination or nutrition levels.

In the case of the multidimensional model, results for each percentile meant using different levels of k (between 3.4 and 9.5).

We use the methodology proposed in Bourguignon et al. (2003), Azevedo (2010), Azevedo and Robles (2010). The latter relaxes the identification assumptions, reducing possible overestimation bias.

The results of the analysis in this section are robust in the sense that they did not suffer significant changes when other weights were used in the multidimensional model.

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Titel: Multidimensional Targeting: Identifying Beneficiaries of Conditional Cash Transfer Programs
verfasst von: Viviane Azevedo
Marcos Robles
Publikationsdatum: 01.06.2013
Verlag: Springer Netherlands
Erschienen in: Social Indicators Research / Ausgabe 2/2013
Print ISSN: 0303-8300
Elektronische ISSN: 1573-0921
DOI: https://doi.org/10.1007/s11205-013-0255-5

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