the survey date.<sup>19,20</sup> On average, pregnant women represent approximately 10 percent of all women aged between 15 and 49. The <u>Addendum gives the pregnancy incidence for each country in the DHS</u> data set.

For children, we use the z-scores for height-for-age (stunting) and weight-for-height (wasting).<sup>21</sup> These anthropometric data are measured for all children aged under 5 in the DHS and LSMS surveys. We then create our measure for stunting (low height-for-age) and wasting (low weight-for-height). A child is deemed to be stunted if his height-for-age z-score is two standard deviations below the median of the reference group; wasting is defined similarly using weight-for-height. Stunting and wasting, while both considered indicators of undernutrition, have different causes and effects. Stunting is an indicator of persistent, longer-term, chronic undernutrition from which it is much harder for a child to recover. Compared to wasting, it is known that stunting has adverse longer term consequences for child development.<sup>22</sup> Wasting tends to be more responsive to short-term (possibly seasonal) food deprivations or illnesses.

Tables 2 and 3 give the summary statistics for the nutritional outcomes for women and children using the DHS and LSMS.<sup>23</sup> Focusing on the larger sample of countries available in the DHS and taking population-weighted averages, we find that 11% of adult women are underweight, while 32% of children are stunted and 9% are wasted (similar numbers are found for children in the LSMS). Across countries, a higher incidence of underweight women is associated with a higher incidence of wasted children (r=0.40, significant at the 5% level<sup>24</sup> ). The correlation between women’s and children’s nutritional status is weaker for stunting (r=0.14).<sup>25</sup> This is what we would expect if a woman being underweight and her children being wasted are caused by similar short-term shocks, while stunting is a more long-term condition.

> 19 Unfortunately, we are unable to exclude pregnant women for Tanzania’s consumption survey, as it did not ask women whether they were pregnant.

> 20 We also dropped observations with missing values for any variables used in the paper, such that sample sizes are consistently the same and comparable throughout the paper. However, we tested the effect of relaxing this constraint and found that it makes negligible difference to the results.

> 21 These variables are already constructed in the DHSs. For the consumption surveys, we use the Stata command zscore06 to convert height and weight values into a standardized value.

> 22 See, for example, Walker et al. (2007) and Hoddinott et al. (2008).

> 23 There are some discrepancies in the means between the two data sets, much of which is likely to do with the timing of the surveys, although differences in sample selection and measurement may also be contributing. 24 For prob.= 0.05, the critical value of the correlation coefficient is 0.306.

> 25 This weak correlation between wasting and stunting is not surprising (Victora 1992). Although there is some evidence that wasting in early childhood can cause subsequent stunting (Richard et al. 2012), the fact that stunting is a longer-term condition while wasting tends to be more transient points to different causative factors.

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