The interdisciplinary literature summarized above is mainly based on self-reported data, which may be subject to social desirability bias (Allen et al. 2003; Minnis et al. 2009).<sup>4</sup> The possibility of bias may be particularly relevant in the case of HIV testing interventions. In a prominent study where offers of VCT were randomly assigned, those offered HIV tests self-report less unprotected sex with casual partners 6 months after the intervention, however there was no corresponding change in sexually transmitted infections (VCT Efficacy Study Group 2000). Reexamining the data, Gong (2013) finds suggestive evidence that the information provided in the pre- and post-test counseling sessions appeared to alter the stigma of reporting risky sexual behaviors at follow-up, leading to a potentially biased estimate of the actual behavior change. Even objectively observed behavior such as the purchase of condoms in Thornton (2008) may be subject to this kind of bias.<sup>5</sup> In addition to these measurement limitations, the effects of testing are likely to display considerable heterogeneity across individuals. One strand of the literature in economics has focused primarily on the informational signal sent, suggesting that HIV tests should only have an effect on those who are ‘surprised’ by their HIV serostatus. In this case, the greatest effect will be found among those who believed they were HIV-negative (HIV-positive) but received a positive (negative) test result (Boozer and Philipson 2000). In fact, these two groups may have offsetting behavioral responses to testing, leading to small or no effects of testing on the entire population: in the extreme, the average treatment effect may be relevant for no one.

While this hypothesis indicates that people will change behavior only as a result of receiving new information, it does not tell us the direction in which behavior will change. The concept of ‘fatalism’ suggests that responses may be non-monotonic: individuals may increase their risky activity in response to increased HIV prevalence around them above a certain threshold (Kremer 1996; Kerwin 2012). Learning one’s own HIV-positive status can be seen as an extreme case,

> 4 In economics, Gong (2013), which examines the effects of a randomly assigned VCT intervention on incident gonorrhea and chlamydia, is a notable exception. In public health, two studies of women presenting at prenatal and pediatric outpatient clinics in Rwanda provide evidence on STI incidence for HIV-positive women (Allen et al. 1992a) and HIV seroconversion for the uninfected partner in cohabiting couples with serodiscordant results (Allen et al. 1992b), but both of these are prospective cohort studies comparing outcomes before and after VCT with no randomly assigned comparison group. The Voluntary HIV-1 Counseling and Testing Efficacy Study Group (2000) states that their RCT was not powered to detect effects on STIs, while another RCT was designed to assess HIV incidence among initially HIV-negative individuals and cannot comment on onward transmission from individuals who were HIV-positive at baseline (Corbett et al. 2007).

> 5 Self-reported data in arguably less sensitive subjects, such as school enrollment (Baird and Özler 2012) and hand washing (Halder et al. 2010; Ram et al. 2010), have also been shown to be subject to social desirability bias.

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