variable in question is equally distributed _(Ck=0),_ the direct effect is zero. Take the case where y is a measure of ill health, with positive mean and negative concentration index. Assume xk also has a positive mean, and has a dampening effect on ill health (6k<O0I. Consider the effect of a rise in xk,; holding constant the degree of inequality in xk-i.e. an equiproportionate rise in _Xk._ The direct effect of this change is a reduction (in numerical value) in the size of _C,_ the reason being that the existing inequality in xk generates more inequality in y to the disadvantage of the poor. But there is an additional effect, operatinJ through the mean. The rise in _Yk_ lowers average ill health, which, holding all else constant, makes for more _relative_ inequality in y (i.e. makes for a more negative C). In this case, the two effects reinforce one another. This will not always be so. Take the case where y is a measure of good health with positive mean and concentration index. Assume Xk contributes to good health and is unequally distributed to the advantage of the betteroff _(C>0)._ The direct effect of an increase in **_xk_** is to raise inequality (C becomes more positive), since the existing inequality in Xk generates more inequality in y. But the rise in _xk_ raises the mean of y which, all else constant, lowers inequality in y. Whether the net effect of the rise in _Yk_ is to raise or lower inequality in y depends on whether xk is more unequally distributed than y itself (i.e. whether _Ck-C_ is positive or negative). Similar remarks apply to the case of a change in _IJk._ Finally, and more straightforwardly, an increase (decrease) in inequality in _Xk_ (i.e. _Ck)_ will increase (reduce) the degree of inequality in y. The impact is an increasing function of _/k_ and **_xk,_** and a decreasing function of p. So, for example, if y is increasina in ill health, C<O, and _xk_ reduces ill health, a rise in inequality in xk will make for a reduction (in numerical size) in C (i.e. C becomes more negative).

# **_3._ Model, data and variable definitions**

In the rest of the paper, we illustrate the methods outlined in the previous section to analyze the causes of malnutrition in Vietnam in 1993 and 1998. Our data are from the 1993 and 1998 Vietnam Living Standards Surveys (VLSS) conducted by the govermment of Vietnam and the World Bank.

We focus on inequalities in stunting (low height-for-age), which we measure using the negative of the child's height-for-age z-score, with the US National Center for Health Statistics (NCHS) data providing the reference.<sup>3</sup> We have two reasons for favoring the z-score over a binary variable indicating whether or not the child in question

> 3 Bhargava and Osmani (1996) have shown that distributions of anthropometric scores can be sensitive to the reference standards chosen. We therefore recomputed our 1998 results using a UK reference scale (Freeman 1995). Although the mean z-score changed slightly, the level of inequality was virtually identical to that obtained using the US reference data-C was -0.110 rather than -0.099. The regression results were very similar too, as inevitably were the decompositions. For example, the UK-based figures for the contributions of household consumption and fixed effects for 1998 in Table 2 were -0.055 and -0.047 respectively.

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