Columns 1 and 4 of Table 7 suggest that _for the sample as a whole_ households are unable to smooth either food consumption or non-food consumption in the face of BMI shocks. Of the two, they are more successful at smoothing food consumption. There is some evidence here, then, for the claim that adverse health shocks in the developing world compromise people’s ability to maintain adequate consumption levels, including consumption of foodstuffs.

Columns 2 and 5 suggest that, if anything, health insurance _worsens_ households’ ability to smooth non-medical consumption in the face of BMI shocks, although the coefficients are not significant. The fact that health insurance does nothing to increase households’ ability to smooth non-medical consumption in the face of health shocks presumably reflects the fact that while health insurance takes care of any medical outlays following a BMI shock, it does nothing to tackle the income losses, which as was seen earlier are appreciable. The hint—and it is only a hint given the size of the relevant _t_ -statistic—that health insurance may worsen households’ ability to smooth consumption would be consistent with a story that relatives and friends are more likely to look favorably on a request for a loan following a health shock if the household does not have health insurance. They may take the view—wrongly, it seems, given the results presented here—that an insured household ought to be able to cope financially in the face of a health shock.

Columns 3 and 4 and 7 and 8 of Table 7 contain something of a surprise, namely that if anything, the poor are _better_ protected than the non-poor from BMI shocks. The difference between the poor and non-poor is significant in the case of non-food consumption, though not in the case of food consumption. The implied coefficients from the model with both sets of interactions (columns 4 and 8) indicate that among poor households—whether insured or not— there is no impact of BMI shocks on either food consumption or non-food consumption. By contrast, among the better-off households, there _is_ a significant effect of BMI shocks on both food and non-food consumption. This is counterintuitive, given that there are apparently no differences between the poor and better off in the income effects of BMI changes, and that the significant effects of BMI changes on medical spending noted earlier were specifically for the poor. The implication is that the poor are borrowing or dissaving to hold food and non-food consumption constant in the face of increased medical spending, presumably considerably so. The puzzle is why they rely exclusively on borrowing and dissaving and do not instead change their food and non-food consumption as the better off do. Possible explanations might include greater access by the poor to informal credit (not implausible but not especially likely either), or the existence of a threshold effect whereby poor households have levels of food and non-food consumption that are simply too low relative to basic needs to enable them to cut back in the face of an adverse BMI shock.

# IV. **REDUCED-FORM RESULTS**

This section reports results of the alternative approach to assessing the impacts of health shocks through an analysis of the correlations of the residuals of the reduced-form equations in eqn (3). Table 8 reports the coefficients of the reduced-form regressions for the full-sample and Table 9 the corresponding residual correlation matrix. The system of reduced-form regressions was then re-estimated for two sub-samples: the poorest 40% without health insurance, and the richest 60% with health insurance.

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