the lagged outcome variable is necessarily uncorrelated with treatment status, but serves to increase precision given the smaller sample size.

The estimates from these specifications are presented in Table 3 for both households and individuals. To account for intra-cluster correlations in the error structure, we cluster standard errors at the household/locality level depending on the unit of observation. The results from the means comparisons are largely replicated with some differences. Most notably, the estimated recall estimates are somewhat lower in the experimental data for the number of individuals reporting at least one acute illness during the month, and the effect on doctor visits declines from 18.3 percentage points to 8.7 percentage points. This decline happens primarily because respondents experienced unusually few health events in the second month of the data. Similarly, the labor impact of the recall period decreases to 1 percentage point since there were fewer work/school days lost in the second month. As a fraction of total work/school days lost in the second month, the recall effect is very similar to what we find in the observational data. It is likely that a longer experimental study would have yielded similar results as those using the observational estimates. The household level estimates for both the observational and experimental estimates are again similar to the means comparisons. Again, the estimated effect of recall on doctor visits and the labor impact in the experimental case is lower due to the inclusion of seasonal effects. In the experimental case, the significance is also lower due to the smaller sample size.

Table 4 explores whether ―forgetting‖ is higher among individuals with larger health burdens. Here, we include an additional interaction of the recall effect with the original morbidity burden in the first year of recall surveys. While for the experimental data, these effects retain their causal interpretation, temporal autocorrelation in the observational data limits our ability to move beyond correlations without imposing further assumptions on the nature of the lag structure. The experimental data strongly suggests that the recall effects are stronger among those with a higher initial burden. These effects are significant for the total number of acute illnesses, the labor impact of sickness and the use of self-medication. They are also reasonably large for doctor‘s visits, but again, the small sample leads to imprecision in the estimated coefficients. In the observational data, the effects are uniformly large and significant and are actually quite close to the experimental estimates. The large size of the coefficients suggests that some portion of the recall effect is driven by those individuals and households with large morbidity burdens. In the observational results, the effect of recall on doctor‘s visits and the labor impact disappears entirely with the inclusion of the lagged morbidity interaction term, suggesting that when individuals and households have a large number of acute illnesses, they start to systematically forget entire episodes and it is precisely these households that drive the large observed differences between monthly and weekly reporting. This is consistent with our hypothesis 11