the marginal cost of validation was roughly $80 per respondent, mainly in wages and transport. By comparison, the marginal cost of surveying a respondent was roughly $70.<sup>30</sup> While this method is considerably more expensive than survey experiments, it is more in line with the depth and cost of commonplace efforts to improve consumption measurement through the use of diaries physical measurement.<sup>31</sup> For crucial measures in large program evaluations, or for statistics informing major policies, the cost is small relative to the intervention, larger study, or larger purpose. For instance, as a proportion of total expenditures on the study, this validation exercise cost under 3% of all research-related costs, and less than 1–2% of program plus research costs.

# **F.8 Further results: Misreporting levels**

Table F.3 reports our proxy of survey over-reporting: the simple survey-validation differences, with p-values from a t-test of the difference from zero. Negative values indicate survey under-reporting, assuming the validator measure is more accurate of course. As noted above, we have the statistical power to detect differences greater than about 17% of the survey mean.

Overall, gambling seems to be slightly underreported in every treatment arm, and highly underreported by men in the control and cash only groups. For instance, 33% of the cash only group admitted to gambling during validation, compared to 13% during the survey. Some of this underreporting could be due to ambiguous behaviors being coded as gambling in validation interviews but not in the survey. But the fact that underreporting is smaller in the therapy arms suggests that the underreporting is not an artifact of different definitions, but rather reflects a strategic response to treatment status.

If we look at stealing, marijuana use, and homelessness, however, none of the survey-validation differences are statistically significant. There is possibly some slight underreporting of drug use and slight over-reporting of stealing, but the magnitudes are generally small in the sense that they are less than 10% of the survey means reported in Table 9. The sample size is small, however, and so many of these differences are not precisely estimated.

We see much stronger evidence of underreporting of expenditures in the survey. The difference for both expenditures is -0.27 in the full sample (Table F.3, Column 6). This difference is large—about a third of the survey mean reported in Table 9. Expenditure underreporting is largest for the video club measure, but both expenditures appear to be underreported. Interestingly, the mean differences appear to be smaller and less statistically significant if the men received one of the treatments. We return to these across treatment arms below.

# **F.9 Further results: Adjusted treatment effects**

We estimate the effect of each treatment on survey over-reporting, in Table F.4. These estimates effectively take the simple survey-validation differences in Panel A of Table 10 and estimate the difference across treatment arms, adjusting for baseline covariates as well as block fixed effects. We use these to calculate an adjusted treatment effect.

> 30Both figures were driven by the fact that it typically took one to two days of searching to find each respondent for surveying, plus the time to survey itself. Both surveying and validating in Liberia were expensive by the standards of household surveys, largely because of the cost of operating in a fragile, post-conflict state and the great difficulties in tracking such an unstable population.

> 31In one extreme example, in the India NSS consumption survey, enumerators physically measure the volume of all food consumption Group (2003).

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