By V. Molini, World Bank; F. Alfani, Food and Agriculture Organization of the United Nations (FAO); A. Dabalen, World Bank; P. Fisker, University of Copenhagen, Changing Disaster
A large literature has documented how households in low income settings suffer short and long run welfare losses from uninsured risk, especially in rural settings where agricultural production risk is prevalent and markets are thin or non-existent. While the short run welfare losses are bad enough, it is now widely acknowledged that the long run losses which typically manifest in foregone investments – in human capital, enterprises, high yielding crops, and so on – are especially damaging.
The concept of vulnerability has gained currency in recent studies of well-being because the static analysis of poverty has been found to be too limiting in capturing the dynamic reality of poor populations: focusing only on the poor leaves out a significant portion of the population who live at a constant risk of becoming poor. Vulnerability is an ex-ante statement about future poverty, before the veil is lifted and the uncertainty is replaced by the knowledge of the actual facts.
However, it has proven a lot easier to define vulnerability conceptually than to measure it. Empirically, since it is a prediction about the future, the ideal data sets – which would involve panel data over several years for each individual (or household) and shocks s/he experienced, responses to the shocks, and the outcomes (e.g. welfare) - rarely exist. Therefore, alternative models that exploit the most commonly available data sets have been proposed in the literature.
The study examines vulnerability to malnutrition induced by rainfall shocks in the Sahel belt of the West African drylands. Five countries are included in the study: Burkina Faso, Ghana, Mali, Nigeria, and Senegal. For Ghana and Nigeria, only territories in the north of these countries lie in the Sahel belt, so the statistics and evidence on welfare losses will apply to households resident in those areas.
First, we estimate the impact of shocks on child health using spatial and historical variation of a measure of drought that is not affected by anthropogenic activities. Next we use the historical and spatial distribution of drought to obtain a distribution of the “expected loss”. This is obtained by multiplying the average effect of a shock with values of our drought measure for each cluster and point in time. This allows us to evaluate the probability that a child in a given location will be malnourished in a hypothetical future period.
The results are shown in Table 4a and 4b. As a point of reference, the tables also show the fraction of children who are stunted and underweight. Roughly 20% of the children ages 1-3 in the West African Sahel belt are stunted and the same figure applies to underweight. The highest shares of children with nutritional deficiencies are found in Northern Nigeria, Northern Ghana and Mali. Senegal has the lowest and the malnutrition rates are lower in urban areas than in rural areas as we would expect.
We find that vulnerability to malnutrition is considerably more widespread than actual malnutrition. For instance, around a third of the child population faces a 50% risk of becoming stunted in the near future compared to the 20% who are already stunted. For underweight the proportion increases to 35%. The places with the largest difference between vulnerability and actual malnutrition are Northern Nigeria for stunting (24 percentage points) and Burkina Faso for underweight (28 percentage points).
Table 1: Actual and vulnerable to underweight and stunting
Finally, we compute the share of the children in each cluster who can be considered vulnerable and plot the results on the map. Figure 10 is a vulnerability map, or cluster level vulnerability estimates. The vulnerability rates range from zero to almost 100%, the latter denoted by red dots. As is evident from the map, and as the tables above show, Senegal has the lowest vulnerability, while the northern Sahel belt – Burkina and Mali – has a substantially higher number of clusters with high vulnerability. Northern Nigeria also has a large number of clusters with high levels of vulnerability.
Figure 1: The cluster level vulnerability maps, stunting (left) and underweight (right)
