# **2 Motivation and related literature**

In the last decade, CCT schemes have become a more and more frequent choice of social policy around the world. While only a handful of CCT programs existed at the end of the 1990s, the 2000s have seen a dramatic increase in the number and coverage of CCT schemes (see Table 1). Until 2009, more than 30 countries had introduced CCT schemes; approximately 32 million households worldwide<sup>7</sup> participated in CCTs with annual transfers amounting to a total of US$ 13 billion.<sup>8</sup> Although the designs of the specific programs vary by country, most CCTs share a common characteristic: poor households receive cash if they comply with conditionalities which are linked to children’s school attendance and (pregnant) women’s and children’s use of health services. In some programs, the number of children per household is also taken into account in determining the eligibility and amount of transfer (e.g. BFP in Brazil).

One of the best-documented programs, particularly in terms of its impacts, is Oportunidades (formerly PROGRESA) in Mexico.<sup>9</sup> The most pronounced differences between Oportunidades and BFP are that the former was accompanied by an intensive effort of impact evaluation<sup>10</sup> as part of its design and that it explicitly focuses on human capital formation. BFP started on a much larger scale (about 6.5 million beneficiary households in the first year), albeit without evaluations as an integral part of its design.<sup>11</sup> Human capital formation is also one of the justifications for the conditionalities in Bolsa Fam ́ılia, but it is not made explicit as its main purpose as in Oportunidades of Mexico (Schady et al. [2009], Soares [2012]).<sup>12</sup> Due to the fact that random assignment of treatment was not part of Bolsa Fam ́ılia’s design, the literature evaluating the impact of BFP can only rely on non- and quasi-experimental techniques to address the problem of potential selection bias. The approaches include Propensity Score Matching (PSM), instrumental variables (IV) and Regression Discontinuity Design (RDD)<sup>13</sup> . Soares [2012] provides a comprehensive review covering some of the main findings on

> 7If we consider that on average a household is composed of 4 people, which is an overly conservative guess for developing countries (in the case of BFP on average a household is composed by almost 5 people (4.7 according to POF 2008-2009)), more than 120 million people worldwide benefit from CCT programs.

> 8The information on CCT schemes, their coverage and total budget is based on Schady et al. [2009].

> 9PROGRESA started in 1997 covering about 300,000 households.

> 10The history of official evaluations of PROGRESA/Oportunidades as well as its schedule of upcoming evaluations are available on the official website of the program (www.oportunidades.gob.mx), starting with a first evaluation report from 1999.

> 11The design of the program took advantage of the knowledge accumulated in previous programs carried out abroad, including PROGRESA and ex-ante simulations (e.g. Bourguignon et al. [2003]).

> 12Soares [2012] argues that one of the reasons is that the program does not necessarily support beneficiaries for the time needed for a generation to complete school.

> 13Pedrozo Jr.’s (2010) thesis is composed by three papers on the relation of BFP eligibility rules and their impact on beneficiaries labor market decision. It is one of the few studies using RD design to evaluate BFP with PNAD data. A drawback of this dataset is that until 2010, it was not possible to identify BFP beneficiaries

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