alone goes a long way in explaining why the poor are less sick than the rich in most self-reported data. In fact, the poor are sicker, a conclusion that is clear in this high frequency data.

Similarly, and possibly more importantly from the policy perspective, this pattern is evident in the number of visits to medical practitioners (Figure 3).<sup>12</sup> Surveys commonly show that doctor‘s visits are positively correlated with income. In Figure 3, this pattern does, in fact, emerge but with some interesting nuances. In the monthly data – the most common recall period in use – there is a clear increase in doctor visits in the lower half of the sample, which plateaus in the middle of the sample. There may be a further increase at the top of the income spectrum, but this is an area with fewer observations. Except for the leveling off – which, unless a demand function had a careful, non-linear, specification of income effects, rather than the standard log–log specification, the regression would miss – our monthly results are similar to standard results.

However, when we turn to the weekly data, an entirely different story emerges. There may be a tiny increase of doctor visits with income at the lowest level of income, though this is neither noticeably nor significantly different from a flat line. Much more prominent is the dramatic **decline** of doctor visits throughout the reliable range of the data. Again, the number of doctor‘s visits may increase at the very top of the income spectrum, but the data is too sparse to tell. The decline in doctor‘s visits as a function of income, when measured in weekly surveys as opposed to the increase when measured monthly, raises suspicions about the conventional wisdom dictating that richer people visit medical practitioners more frequently.

Finally, we examine how recall and PCE affect health care expenditures. One issue that limits our ability to present a full characterization of the impact of recall periods on health expenditures is the sparse data on very high, catastrophic expenditures that can significantly alter the mean and are difficult to capture fully in this kind of micro-study. The impact of recall on expenditures and the limitations arising from the small sample are easily seen in Figures 4 and 5, which show total health expenditures by income. Figure 4 uses our whole sample, while Figure 5 caps health expenditures at Rs.1000 a month.

Figure 4 is difficult to interpret. There is no consistent difference between weekly and monthly recall given that the curves cross four times. However, inconsistencies in the curves are driven almost entirely by a tiny number of large expenditures. We hypothesize that these are particularly memorable events for which the monthly and the weekly recall quality is not likely to vary. When we group households into income terciles, the poorest group, with an average monthly per capita expenditure of Rs. 600, reports a single expenditure

> 12 The term ―doctors‖ has to be interpreted carefully here since the range of providers of medical services is very wide in India – from MD‘s and MBBS (standard medical university training) doctors to people knowledgeable of Indian traditional medicine (but prescribe allopathic medicine anyway) to genuine ―quacks‖ with no training whatsoever (Das and Hammer, 2005).

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