# **2. Literature review**

Studies on the determinants of participation in antipoverty programs have typically focused on public works programs. There is strong empirical evidence confirming a negative relationship between household wealth and participation in these programs (see, for example, Chirwa, Zgovu, and Mvula 2002; Gaiha 1996; Jalan and Ravallion 1999). But this negative association is driven by the design of the workfare programs, which were aimed at facilitating the self-selection of unemployed workers from usually poorer families.

More recently, a number of empirical studies have argued that poverty is associated with a higher probability of participating in CCTs. Heinrich (2007) presents evidence of the positive effects of Argentina’s _Becas Estudiantiles_ CCT program on student outcomes. In the first-stage regression of this paper, the author finds that students in households with a per capita income below a certain threshold (Arg$45 per month) were significantly more likely to participate. However, after controlling for an index of basic needs, the author finds that students in households with _lower_ per capita incomes were less likely to participate in the CCT program, a result that is in line with the framework outlined in this paper.<sup>8</sup> In a related study, Oosterbeek, Ponce, and Schady (2008) analyze the impact of Ecuador’s _Bono de Desarrolo Humano_ CCT program on school enrollment. They report the results of a regression of actual treatment status on background characteristics and find that poorer people are more likely to receive the transfer. These authors incorporate a third-degree polynomial of the poverty index in the children’s outcome estimation, but they only use the linear poverty index in the regression of actual treatment.

Two studies using the Urban Evaluation Survey (Encelurb) of Mexico’s _Oportunidades_ CCT program investigate the extent to which the urban component of _Oportunidades_ affects children’s outcomes and household consumption. Angelucci and Attanasio (2009) estimate a linear probability model of program participation among eligible households in treatment areas that incorporates second-degree polynomials for the poverty level as well as household income and consumption variables. They find that a household in the 75th percentile of the poverty distribution is 69 percent more likely to be a program participant than a household in the 25th percentile. They also conclude that participation is inversely related to both consumption and income. In addition to data on household income, consumption, and poverty level, their model includes information on transitory shocks and the local availability of schools and health centers. However, their model has some data limitations because it does not include key determinants of household decisions to participate in _Oportunidades_ , such as proxies for the relative price of schooling, parental preferences, and the opportunity costs of participating. Although their results do not directly support the outline presented in this paper, they conclude

> 8 Heinrich (2007) constructed an index of need using 20 measures from base data that include: dependents, household head occupation, whether household head is pregnant, type of home/tenancy/living conditions, distance to school, years of education of all household members, student hours worked outside/inside home, student age-grade difference, illness or disability, and household income.

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