security, which are measured in different units, we standardize the food security measures so that the estimated parameters are actually beta coefficients.<sup>4</sup> We consider height-for-age, which is a measure of chronic malnutrition, as well as weight-forage, which is measure of underweight, as our dependent variables of interest. The rationale for considering these two is to try and understand if there are meaningful differences in the way the various food security indicators relate to different states of malnutrition. To the extent that the state of being underweight is largely a reflection of caloric deficiency, it is _a priori_ plausible to imagine food security measures that are able to better capture the quantity of the diet to be better related with weight-for-age. On the other hand, measures that capture elements of quality of the diet may be better suited for the purposes of either targeting or monitoring of interventions related to chronic malnutrition.

Analyzing a longer term measure of health such as height-for-age alongside measures of food security that are generally constructed using dietary information at the household and individual levels from 24-hour, 7-day, and 30-day recall periods raises the usual question of time consistency. How can nutritional outcome measured by height-for-age, which is the product of long-term cumulative processes, be related to what a child ate yesterday or over the last week? The argument there is that recent dietary information can be meaningful as a contributor to a child’s linear growth to the extent that it reflects dietary patterns and nutrient adequacy over longer, biologically meaningful period of time (Arimond and Ruel, 2004). Therefore, the analysis is carried out based on the assumption that, under normal circumstances, households’ recent dietary patterns are a good proxy of their long-term food consumption behavior.

This assumption will be too restrictive if the period of the survey is particularly unusual for any reason (e.g., economic shocks such as drought) or if there are strong seasonalities. However, this possibility is somewhat mitigated by the fact that the surveys that we use for the five countries we analyze were mostly during what appear to have been normal years.

In order to gear our analysis to make it directly applicable to policymakers interested in either carrying out nutritional targeting or monitoring of the nutritional impacts of agricultural projects, we conduct it under two different regimes. This essentially amounts to estimating the above equation (1) with and without the conditioning variables, _Xih_ .

If our goal is to find an indicator suitable for nutritional targeting, we want to run the specification without the conditioning factors. This is because in this case our interest will primarily be in picking out the food security indicator that best predicts the nutritional outcomes. The mental exercise that underpins this idea is the following: How confident can we be that a household is nutritionally insecure based on what we observe the household food security status is (based on various measures) _if we have no other information on the household_ ? This is similar in principle to proxy means testing in the 8