enjoy better access to infrastructure facilities and sanitation systems such as flush toilets and piped water. Employment rates are not substantially different across income groups, but the composition of employment does vary with a higher incidence of public sector employment among the middle and upper income groups.

Since households in the experimental sub-sample are those who could still be located eight years later, Appendix Table 1 compares the main outcome variables for the original sample of 300 households to the experimental subsample of 205 households. The means are computed for the first wave of weekly surveys completed in 2000-01. The number of ―acute‖ illnesses refers to the total number of acute illnesses reported in the preceding month, and weekly reports are aggregated up to obtain monthly totals. Since aggregated measures could overstate the recall effect if episodes last longer than a week, we will also examine two other outcome measures—whether or not at least one acute illness was reported and the duration of acute illness— that should not be affected by this aggregation bias. Our measure of the labor and schooling impacts of sickness are based on reports indicating that at least one day of work (or school) was lost due to an acute illness during the relevant recall period; for brevity, we refer to this variable as the ―labor impact of acute illness‖.

For our full sample, based on the weekly data, individuals report just less than 1 acute episode a month, and about 47 percent report at least one episode. The average time spent sick every month is 4 days and individuals report losing at least one day of work/school in 7.8 percent of weeks due to an acute illness. Das and Sánchez (2004) report the types of acute illnesses that were reported during the weekly surveys; the large majority is respiratory illnesses, non-specific myalgias, dermatological disorders and ENT infections. Given that (a) doctors very seldom tell their patients what they are suffering from and (b) even when they do, they is a high probability that they are incorrect, it is unlikely that these reports can be used to deduce what the underlying condition is, including the severity of the illness.<sup>8</sup> Whether or not treatment was sought is measured through doctor visits and self-medication. On average, people visit 0.4 doctors a month (roughly 1 doctor‘s visit every 10 weeks) and the majority of visits are for acute illnesses. Individuals also self-medicate, and around 30 percent self-medicated at least once in an average month of the survey.

Finally, there appear to be few differences in the initial year of the survey between the larger observational sample and the experimental sub-sample of 205 households, although there is some evidence that more time is spent sick with acute illnesses with higher reported durations in the experimental sub-sample. In addition, the sub-sample households were somewhat more likely to self-medicate and reported somewhat higher labor

> 8 Das and others (2011) send standardized patients to doctors in urban Delhi and rural Madhya Pradesh. Across all interactions, a diagnosis was articulated in 31.3 percent and the rate of correct diagnosis conditional on a diagnosis being given was 8.8 percent for myocardial infarction, 22.5% for asthma and 9.5% for dysentery.

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