effectiveness of CCT programs when compared to traditional supply side investments in health and education has not been sufficiently explored (Lagarde, Haines, and Palmer, 2008).

In any case, these cost analyses would provide little insight into the costs of implementing a CCT-based program for HIV prevention where the major expenditures would include the cash payment (for which the appropriate magnitude has not been determined), as well as laboratory costs associated with the monitoring of biological outcomes. Petry (2000) has indicated that having an on-site laboratory is essential element of providing immediate and reliable feedback, which dramatically increases the capacity constraints for implementing such an intervention in rural areas of Sub-Saharan Africa. Improvements in rapid diagnostic testing may eventually address this concern.

However, cost concerns must be balanced against the benefits of an effective behavioral intervention, and – again – too little is yet known to draw any conclusions at this stage. Certainly the epidemiological context is highly relevant to this discussion, as the costs can be more easily justified in settings where the rate of disease transmission is very high. Also, the size effect of the CCT on infection rates is relevant.

De Janvry and Sadoulet (2006) have raised concerns about inefficiencies in the design of CCT programs, pointing out that large-scale CCT programs fail to distinguish between those families who would have attended pre-natal clinics and sent their children to school regardless of the incentive and those who require the incentive to induce the desired behavior. They conclude that CCT programs can be made more efficient if they do a better job of targeting the group that needs the incentive to be induced to alter their behaviors.

In other words, the less targeted the program, the less efficient it will be. This poses a challenge for the targeting of a CCT program to address risky sexual behavior. Should it target a geographical area, where HIV transmission rates are high; or, should it target a specific demographic group, such as young people between the ages of 15 and 30, where infection rates are highest; or occupational groups, such as sex workers or truck drivers? Cost-effectiveness and cost-benefit studies of CCTs in different target groups will be needed to inform these types of implementation decisions.

_Monitoring and Evaluation_ Monitoring and evaluation of CCT programs to address risky sexual behavior also raises some very special challenges. Although the ultimate aim is to address HIV transmission in the target population, the immediate objective is to reduce risky sexual behavior. A proxy measure is therefore needed, since the immediate outcome of interest in not directly observable. In HIV prevention, prevalence rates, incidence rates, and selfreported behaviors relating to sexual practice (e.g. condom use, number and concurrency of sexual partners) are commonly used for this purpose; by they are not perfect measures by any means. For example, biological markers such as HIV incidence are highly dependent on the prevalence and transmission patterns of the virus circulating within the population. Since HIV infection is a rare event compared to other types of STIs, the sample sizes needed to detect difference in study arms can be prohibitive. Also, selfreported data, especially as concerns sexual behavior, are notoriously unreliable and subject to strong reporting biases. Thus, it has become common practice in studying the impact of HIV prevention programs to track the incidence of STIs that are known to be 15