60 CHAPTER 4
and attendance and the use of health services, because with UCTs such par- ticipation would be optional rather than mandatory and because supply-side interventions—such as building schools or contracting NGOs to deliver health services—sometimes accompany CCTs. However, a UCT could also provide the cash needed for school fees or transportation or to compensate for lost child labor, increasing school attendance without the conditionality. Although we know that cash transfers can have an impact on human capital (see Chapters 6, 7, and 8), we do not know the relative importance of the different mech- anisms through which either CCTs or UCTs work. Even rigorous CCT evalua- tions have presented results as a “black box,” studying the combined effects of all components on a given outcome without assessing which components are responsible for which outcomes (Burtless 1995; Heckman and Smith 1995, cited in de Brauw and Hoddinott 2008). Conditionality is one of those components; we know little about whether, to what extent, and under what conditions conditionality would be responsible for increasing a particular outcome. Evidence is beginning to emerge, however, and new research is being
designed to answer this question. Simulating the impact on school enrollment of the CCT Bolsa Escola in Brazil and that of a UCT, Bourguignon, Ferreira, and Leite (2003) conclude that the main enrollment impact is due to the condi- tionality: among ten- through fifteen-year-olds not in school, about 60 per- cent enroll in response to the program, whereas a UCT has no effect. Using a model for Mexico’s CCT PROGRESA, Todd and Wolpin (2003, cited in de Janvry and Sadoulet 2006) attribute 80 percent of the program’s impact on school enrollment to the conditionality and 20 percent to the income effect. Using data from Mexico, de Janvry et al. (2006, cited in de Janvry and Sadoulet 2006) also reach a similar conclusion on conditionality, finding that one dollar of CCT income is about eight times more effective in inducing enrollment than a dollar of UCT income at the mean income of the poor. Two other studies take advantage of “accidental experiments” to assess
conditionality. Data reflecting widespread implementation errors, such that transfers were not conditioned or people thought they were not, were used to construct a group of beneficiaries receiving “unconditional” transfers to compare with a group of beneficiaries receiving “conditional” transfers. The first study, by de Brauw and Hoddinott (2008), takes advantage of the fact that in Mexico’s PROGRESA, some beneficiaries did not receive the forms needed to monitor school attendance. If the form was not received, attendance could not be monitored. In order to control for the fact that some households without the form might still have thought that attendance was required, this group was further divided into those who listed (on the evaluation survey) school atten- dance as a condition and those who did not. A number of statistical techniques were used to ensure that the results were not due to unobserved differences
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