1. V ULNERABILITY TO M ALNUTRITION IN THE W EST A FRICAN S AHEL F EDERICA A LFANI 1,2, A NDREW D ABALEN 1, P ETER F ISKER 3, V ASCO M OLINI 1 1 World Bank 2 FAO 3 University of Copenhagen 2 nd International Conference on Sustainable Development in Africa Dakar, November 26-27, 2015

2. Outline of the Presentation Motivations and background of the study Data sources Theoretical framework Empirical application to five West African Countries Burkina Faso Ghana Mali Nigeria Senegal Preliminary results Conclusions

3. MOTIVATIONS While measures of poverty are being widely used for policy making (i.e. as a targeting tool for social protection), measures of vulnerability are mostly present in the economic literature. There is a growing demand, however, for a way to put numbers on the risk of deprivation since many countries are prone to weather shocks and other aspects that make living standards volatile. The economic literature on vulnerability is large and somewhat confusing - no consensus has emerged on a clear definition.

4. Background of the study Malnutrition (stunting and underweight) among children is highly prevalent throughout the Sahel region. The region is also often affected by lack of rainfall and rapidly changing food prices. To explore the concept of vulnerability to malnutrition using multiple rounds of surveys merged with geospatial data on weather shocks in such a way that: the measure is based on a simple and understandable methodology the outcome is valid and reliable the measure is relevant to policy makers

5. Who are the Vulnerable? Population Vulnerable at 25% risk Vulnerable at 10% risk Stunted / Underweight

6. Concepts of Vulnerability The empirical literature on vulnerability can be divided into three different strands assessing the phenomenon: 1.Vulnerability as expected poverty (VEP) Involves the exposure to a bad event – or negative shock – that has not been realized yet. The probability of falling into poverty after the negative shock has occurred. The most common measure of vulnerability in the empirical literature Examples provided by Chaudhuri et al. (2002), Chaudhuri (2003), Christiansen and Subbarao (2005), Calvo and Dercon (2007, 2013), Zhang and Wan (2009), and Dang and Lanjouw (2014). Since the future is uncertain, the degree of vulnerability increases with the length of the time horizon (Prytchette, Suryahadi and Sumarto, 2000).

7. Concepts of Vulnerability (cont.ed) 2.Vulnerability as (low) expected utility (VEU) Based on expected utility theory (Ligon and Schecter, 2003). Consumption variability Shocks Vulnerability Risk aversion 3.Vulnerability as the ability to Smooth Consumption in response to shocks (VSC) Ex-post assessment of the extent to which a negative shock causes a deviation from expected welfare. It is not a vulnerability measure because there is no probabilities' construction.

8. Practical limitations to estimation The two most common ways of conceptualizing vulnerability are the Vulnerability to Expected Poverty (Christiansen and Subbarao, 2005), and Vulnerability to Expected Utility (Ligon and Schechter, 2003). Theory works with panel data, but in most of the cases only cross-sections are available. Methods that are developed for cross-sections leave out important aspects of the concept of vulnerability. When shocks are included, many times they are self-reported. This could lead to endogeneity problems since drops in welfare might cause shocks to be experienced harder.

9. Data Sources Demographic and Health Survey (DHS) In this study we use geo-referenced DHS data from 5 West African countries: Senegal, Mali, Burkina Faso, Ghana, and Nigeria. For Ghana and Nigeria we only look at the northern provinces since the study is focused on the Sahel region. Only rural households are included. Our unit of observation is the child, aged between 0 and 3. 2003200520062008201020122013Total Burkina Faso 9,4580007,1240016,582 Ghana 1,188009190002,107 Mali 0012,836005,205018,041 Nigeria 3,6600016,9120018,96139,533 Senegal 03,351004,536007,887 Total 14,3063,35112,83617,83111,6605,20518,96184,150 Table: Observations by country and year

10. Data Sources (cont.ed) Weather shock The indicator employed as a proxy of a weather shock variable is the so-called Predicted Greenness Anomaly, a composite drought index based on: the Normalized Difference Vegetation Index (NDVI), a measure of the greenness ground cover from the MODIS Terra Satellite; Accumulated monthly rainfall from the Tropical Rainfall Measuring Mission; Daily temperature from the MODIS Terra Satellite. Predicted Greenness Anomaly is estimated in the child’s year of birth for stunting and in the year preceding the survey for underweight. Calculated for the 5 countries included in the analysis and standardized to mean 0 and SD 1 over the period 2000-2013.

11. Data Sources (cont.ed)

12. Estimation Strategy Step-by-Step methodology 1.Calculate the effect of a shock on height-for-age and weight-for-age Z- scores. 2.Calculate the expected loss in Z-scores for each counterfactual birth-year using the historical distribution of weather shocks and the average effect of a shock. 3.Derive the share of periods for which the children would have been malnourished (i.e. HAZ or WAZ < -2 SD the WHO international reference median), had they been born in a random year. 4.Aggregate over clusters and countries to get vulnerability shares at different risk-levels.

13. Step 1: The Effect of a Shock on HAZ and WAZ We model the effect of a weather shock using a pooled OLS and estimating the following equation: Either Height-for-Age or Weight-for-Age Z-scores capturing long and short term malnutrition. Explanatory variables at individual and household level Village level (standardized) Predicted Greenness Anomaly during the growing period of the year when the child was born (for stunting) or in the year preceding the survey (for underweight). Set of dummies to account for some specific country and provinces effects.

14. Step 1: The Effect of a Shock on HAZ and WAZ (cont.ed) Table: Pooled OLS VARIABLES (1)(2) HAZWAZ Predicted greenness index14.81***14.08*** HH has toilet5.26*2.45 HH size-0.99***-0.82*** Primary education17.87***18.83*** Secondary education31.46***40.46*** Higher education56.46***62.15*** HH has radio3.783.11 HH has TV15.63***15.94*** HH has refrigerator13.19**8.86** HH has bicycle-5.41**-6.30*** HH has car15.35***13.42*** Dwelling has good floor17.85***15.27*** Age of HH head0.21**0.11 Male headed HH-6.79*1.40 Current age of child8.36**13.80*** Observations31,995 R-squared0.1720.210

15. Step 2: The Expected Loss in Z-scores for Each Counterfactual Birth-Year To compute the expected loss both in HAZ and WAZ scores We calculate the distribution of weather shock variable for each cluster standardized with mean 0 and SD 1. no positive gains assumption We impose the no positive gains assumption we only look at potential losses from weather shocks (i.e. no catch-up when the harvest is better-than-average). For each growing period, we multiply the value of the shock variable with effect of shock.

16. Step 3: The Probability of Being Malnourished We finally calculate the malnutrition status of each child hypothetically for each year of the shock data spans (2000-2015) by subtracting the expected loss from the actual HAZ and WAZ scores of the child. The share of periods in which a child would be malnourished indicates his or her vulnerability to malnutrition.

17. Step 4: Vulnerability Shares at Different Risk-levels Stunting50 % risk25 % risk10 % risk5 % risk Burkina Faso0.4110.4120.4470.4840.501 Ghana0.3880.3890.4340.4700.491 Mali0.4070.4080.4420.4740.493 Nigeria0.4450.4470.4680.4880.498 Senegal0.2900.2910.3270.3600.375 Total0.4160.4180.4460.4740.488 Underweight50 % risk25 % risk10 % risk5 % risk Burkina Faso0.334 0.3730.4070.420 Ghana0.2580.2590.2830.3220.340 Mali0.2980.2990.3280.3650.383 Nigeria0.3810.3820.4050.4270.438 Senegal0.2090.2110.2400.2660.280 Total0.3370.3380.3660.3950.408

18. Step 4: HA Shares at Different Risk-levels

19. Step 4: WA Shares at Different Risk-levels

20. Conclusions A novel very simple methodology to estimate children vulnerability to stunting and underweight in a set of Western Africa Sahel countries. We estimate that 41% of the children in our sample faces a 50% risk to be stunted and 33% face a 50% risk to be underweight in the near future, partly as a consequence of exposure to weather shocks. 50% risk almost coincides with the actual value of stunting because those who have currently nutritional deficiencies are, in presence of a negative shock, very likely to remain stunted or underweight. The comparison of different countries shows some heterogeneity. The highest shares of children vulnerable to stunting are found in Northern Nigeria, Burkina Faso and Mali. Senegal has the lowest vulnerability rates within the selected group. Very similar results are obtained for underweight: Northern Nigeria, Burkina and Mali tend to perform much worse than Northern Ghana and Senegal.

21. Discussion This study cannot be viewed, nor it is presented, as conclusive The number of vulnerable is not larger than the actual number of malnourished - some possible reasons: We only consider one source of variation, namely weather shocks. The effect of a shock is measured with a noise (i.e., all factors that are not-climatic, such as cultivation, irrigation and urban expansion, may affect the greenness of the planet) that will bias the estimate downwards. Policy relevance might be higher if combined with poverty mapping methods

22. Thank you! Federica Alfani (federica.alfani@fao.org)