was stunted (i.e. two standard deviations or more below the NCHS mean). First, it conveys information on the depth of malnutrition rather than simply whether or not a child was malnourished. Second, it is amenable to linear regression analysis (Lavy et al. 1996; Thomas et al. 1996; Ponce et al. 1998; Appleton and Song 1999; Alderman et al. 2000)-this is essential to our decomposition method.<sup>4</sup> The use of the z-score in the analysis of inequality does require that we accept the value judgment that "taller is always better", but this seems relatively innocuous.5 We use the _negative_ of the z-score to make our malnutrition variable easier to interpret-it is increasing in malnutrition, and in both years has a positive mean.<sup>6</sup> Like Ponce et al. (1998) we confine our attention to children under the age of ten, there being evidence that over the age of nine genetic factors start to seriously constrain growth (Martorell and Habicht 1986; Kosternans 1994). We have 5067 children under the age of ten in 1993, and 4796 in 1998.7 Mean values of (the negative of) the height-for-age variable in the 1993 and 1998 samples were 2.036 and 1.608 respectively, indicating an appreciable improvement in average nutritional status between 1993 and 1998.8 To compute the concentration indices, we ranked children by per capita household consumption in 1998 prices.<sup>9</sup> Our concentration indices for 1993 and 1998 were -0.077 and -0.099 respectively, indicating a concentration 'of malnutrition amongst the poor in each year, and an appreciable worsening in inequality between 1993 and 1998.

To explain variations in height-for-age, we adopt a standard household production-type anthropometric regression framework (Lavy et al. 1996; Thomas et al. 1996; Ponce et al. 1998; Appleton and Song 1999; Alderman 2000), in which the (negative of the) child's height-for-age z-score is specified to be a linear function of a _4We_ are, in fact, able to introduce some nonlinearities-we have a squared term for one variable, and enter another in logarithmic form.

_5_ The same cannot be said, of course, of the analogous value judgement for weight-for-age. For both stunting and underweight, one could apply the methods outlined above with a malnutrition deficit variable, defined as the gap between the actual z-score and the threshold used to define malnourished children (minus two standard deviations below the mean of the original z-score). We did this in the case of stunting, and obtained broadly similar results to those presented here.

**_6_** Our method would break down if the mean were zero, which it would be if one used anthropometric z-scores on the NCHS children. This group of children is not, however, a representative sample of the US children, the NCHS data on children age 0-36 months of age having been collected over a long period of time from a population of middle-class, white, bottle-fed Americans. The problem of a zero mean even on these children would not, of course, arise if one used a malnutrition deficit variable rather than the z-score.

7This excludes children with missing information on any of the variables included in the regression model. _8_ We employed sample weights in computing the means for the 1998 survey, since the 1998 LSMS-unlike the 1993 LSMS-is not representative without weights.

9 We also employed sample weights in generating the ranks in the consumption distribution for the 1998 data (they were unnecessary for the 1993 data). For both years, concentration indices were computed using the convenient covariance method (Jenkins 1988), using sample weights in the case of 1998.

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