vulnerability to malnutrition in the west african...

Policy Research Working Paper 7171

Vulnerability to Malnutrition in the West African Sahel

Federica AlfaniAndrew Dabalen

Peter FiskerVasco Molini

Poverty Global Practice GroupJanuary 2015

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Produced by the Research Support Team

Abstract

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

Policy Research Working Paper 7171

This paper is a product of the Poverty Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at [email protected].

This study estimates marginal increase in malnutrition for children ages 1–3 years from exposure to an extreme shock in the West African Sahel. The study uses knowledge of a child’s birth and high resolution spatial and temporal distribution of shocks, calculated from the Normalized Difference Vegetation Index and satellite-based measures

of rainfall and temperature to link a child to the shock experienced in-utero. The study finds that while around 20 percent of the children in the sample are stunted or underweight, more than 30 percent of the children in the sample are highly vulnerable to either form of malnutrition.

Vulnerability to Malnutrition in the West African Sahel

Federica Alfani, Andrew Dabalen, Peter Fisker and Vasco Molini

Key words: Malnutrition, Vulnerability, Shocks, Sahel

JEL Classification: I31, I32, I140

F. Alfani, Food and Agriculture Organization of the United Nations (FAO); A. Dabalen, The

World Bank; P. Fisker, University of Copenhagen, Changing Disaster; Vasco Molini, The World

Bank.

Acknowledgement: We would like to thank Yeon Soo Kim for excellent support with data analysis. This study

was funded by the Regional Studies Program of the Chief Economist of the Africa Region.

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1. INTRODUCTION

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 nonexistent (Townsend, 1994; Karlan et al.

2013; Udry, 1994; Jacoby and Skoufias, 1997). 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 (Dercon and Christiaensen, 2011; Alderman, Hoddinott and Kinsey, 1996;

Morduch, 1990; Hill, 2009). The actions that are taken by households in these contexts to avoid

high risk but high return activities are motivated by their desire to reduce their vulnerability to

shocks.

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.

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One group of authors defined it as the probability of falling into poverty (Christiaensen

and Boisvert, 2000; Christiaensen and Subbarao, 2005; Chaudhuri, 2001; Pritchett et al. 2000),

and more recently by Dang and Lanjouw (2014). An alternative definition given by Ligon and

Schechter (2003) defines vulnerability as the difference between expected utility and a level of

consumption that is assured (a level of consumption where there is no risk), while Dercon and

Krishnan (2003) propose vulnerability as uninsured exposure to risk. Most of these measures of

vulnerability share three elements in common. First, there is a basic acceptance that vulnerability

involves exposure to a bad event – that is, a negative shock – that has not yet been realized.

Second, there is a non-negligible probability that in the event of the shock there will be a loss

(income, consumption, health, and so on). Finally, they all define income thresholds that classify

households into vulnerable and non-vulnerable. However, there is no consensus on what the

threshold income that assigns households into vulnerable or not vulnerable should be. For

instance, most of the papers that define vulnerability as future or expected poverty assume an

income threshold at which a household has 50% probability of falling into poverty, although

Dang and Lanjouw propose a 10% probability.

This study places itself in the category of estimating vulnerability to expected poverty.

Unlike the existing studies, this paper 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. For other countries, we look at the entire sample. For

the rest of the paper, Sahel will refer to these countries.

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Our approach follows the methods proposed by Anttila-Hughes and Hsiang (2013). 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. We find that approximately 20% of children on

average, in the five countries, are malnourished and that the uncertainty about the future in

combination with the effects of negative weather shocks means that the fraction that is

vulnerable to malnutrition lies between 30% and 40 % of the children in the sample.

The rest of the paper is organized as follows. Section 2 provides a short description of the

risk environment. Section 3 lays down a simple model to estimate the damage to child health

(malnutrition) and build on that to estimate vulnerability of children to droughts. Section 4

describes the data we use to estimate welfare impacts of and vulnerability to droughts, while

section 5 discusses the results. In section 6 we conclude.

2. RISK CONTEXT OF THE SAHEL

Households in the Sahel belt face many risks. Some of these risks, when realized,

diminish human capital of the households on a frequent basis (e.g. a subset of idiosyncratic

health risks), and thereby affect their productivity. In the most extreme, physical destruction of

human capital happens when there is a large scale conflict or high mortality epidemic. Other

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risks affect production directly, such as pests, droughts and floods, when they destroy crops and

livestock assets.

One persistent risk that has come to be associated with the belt is rainfall failure. The

drying of the Sahel, and subsequent changes to social organizations and livelihoods, has been a

steady process that began in the 1950s, and constitutes one of the most consequential changes in

observed global precipitation in the 20th century (Nicholson, 1993; Dai et al., 2004). Figure 1

shows the now familiar evolution of precipitation for the Sahel region, dating back a century,

characterized by three distinct periods. The first period, between 1900 and 1930, was

characterized by substantial variability in rainfall relative to the long term average rainfall. This

was followed by a relatively long wet period in the 1950s and 1960s, when the rainfall averages

were above the long run mean (positive values). Then beginning around the 1970s, and through

the 1990s, things turned for the worse when there occurred a prolonged period of unusually dry

spell (negative values). While there has been some recovery since the 1990s, the variability

remains high and more importantly, during the decade of the 2000s, there is yet no return to the

relatively favorable precipitation trends of pre-1970.

Figure 1: Sahel precipitation, 1900-2013

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Source: The Sahel precipitation index: doi:10.6069/H5MW2F2Q

But rainfall failure (drought) is just one of many shocks that erode households’

capabilities. In fact, part of the reason why the shocks in the Sahel tend to be devastating is

because they are highly correlated. Even in normal times, when rainfall has not failed and

harvests have been good, the stock of quality land, and the existing production technology, is not

enough to meet the nutritional needs of large fractions of the rural population. These correlations

are very well established for rainfall and price shocks. Figure 2 plots rainfall and price trends for

the six study countries in the 2000s. The rainfall values are averages for each month, over small

areas (grids) collected by satellite data. Prices were obtained from markets in each country by the

Famine Early Warning System (FEWS) and Vulnerability Assessment and Mapping (VAM) of

the World Food Program (WFP). Although we have the rainfall data since 2000, the price data

collection does not go back that far for each country, so for each country we plotted the periods

when both data sets are available. Figure 2 shows the resulting trends for Burkina and Northern

Ghana. The maps for the other countries are in Annex 1.

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Figure 2: Rainfall and price Graphs here

Source: Authors’ own calculation using VAM and TRMM data.

As the graphs show, the rainy season for all these countries falls around the same time of

year – June to September. The exception is northern Ghana where the wet season starts one

month early, and ends a month later – that is, the rainy season lasts from May to October. The

graphs show that for many of the countries, price volatility is very high and highly correlated

with the growing season. There is a tendency for prices to fall sharply immediately following the

harvest – probably because there is a huge surge in supply in the market – and then rise slowly

and reach a peak just before the rainy season, especially for certain key staples. There are also

two additional observations. One is that periods of droughts are notable for the sharp increases in

prices, not surprisingly. This is the case in 2002, 2005 and 2010, especially in Burkina Faso. The

other is that the global food price shocks in 2008 and 2009 show up in these price data for

countries where there is substantial dependence on imports to meet the main staple, as is evident

in Nigeria and Senegal for rice. But even in a country like Mali, where domestic rice production

is substantially higher, there was visible upward pressure on domestic prices from international

transmission of shocks.

50

100

150

200

250

Average nominal price(XOF/KG)

100

150

200

250

300

Rainfall(mm)

2000m7

2001m7

2002m7

2003m7

2004m7

2005m7

2006m7

2007m7

2008m7

2009m7

2010m7

2011m7

2012m7

Average monthly rainfall Maize

Millet Sorghum

Burkina Faso

050

100

150

200

Average nominal price(G

HS/unit*)

100

150

200

250

Rainfall(mm)

2008m7

2009m7

2010m7

2011m7

2012m7

Average monthly rainfall Cassava

Maize Plantain_Apentu

Rice_Local Yam

Ghana

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Finally, the interactions of known and “new” shocks have raised the scope of potential

welfare losses. One of those new shocks is open communal conflict. To be clear, the Sahel and

especially these six countries, like most of Sub-Saharan Africa, have had their periods of

political instability, characterized by coups and low scale violence. But as Figure 3 shows, there

has been a major spike in conflict in some of the countries in recent years. Furthermore, these

conflicts seemed to be more common during the dry season – perhaps because mobility of

combatants is easier – when the populations are already under stress.

Figure 3: Conflict days and weather in Sahel West Africa

Source: Authors’ own calculation using ACLED data.

A look at self-reported shocks from recent surveys confirms the multiplicity of shocks

that matter to households. More than half the households consider price and weather shocks as

the most important shocks in their lives. Furthermore, the share of households who report

experiencing two or more shocks ranges from a low of 6 percent in Senegal to 50 percent in

01

00

20

03

00

40

0

1997 2002 2006 2010 2014 1997 2002 2006 2010 2014 1997 2002 2006 2010 2014

Mali Nigeria Senegal

Year

Dry season

05

01

00

15

02

00

1997 2002 2006 2010 2014 1997 2002 2006 2010 2014 1997 2002 2006 2010 2014

Mali Nigeria Senegal

Year

Rainy season

Note: Different scale

Total conflict days

02

04

06

0

1997 2002 2006 2010 2014 1997 2002 2006 2010 2014 1997 2002 2006 2010 2014

Burkina Faso Ghana Niger

Year

Dry season

01

02

03

04

0

1997 2002 2006 2010 2014 1997 2002 2006 2010 2014 1997 2002 2006 2010 2014

Burkina Faso Ghana Niger

Year

Rainy season

Note: Different scale

Total conflict days

9

Burkina Faso. In this paper we look at the impact of rainfall shocks in six countries. In the next

section we explain the empirical strategy.

Figure 4: Share of households reporting two or more shocks

The next section describes the data we use and how it is spatially linked. The two most

important data sets for this particular analysis are the Demographic and Health Surveys (DHS)

and our measure of shocks.

3. DATA DESCRIPTION

3.1 Demographic and Health Surveys (DHS)

The DHS are cross sectional surveys, funded primarily by USAID and implemented until

recently by Macro International1 and local statistical agencies, which are designed to collect

information mostly on maternal and child health across many countries in the world.

Our analysis in this paper includes five countries in West Africa, each with at least two

rounds of geo-referenced data for the period between 2000 and 2012. The data are representative

at the national level as well as at rural and urban locations. Our unit of analysis is children who

1 The implementation of the DHS is now overseen by ICF, which recently bought Macro International.

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were born 1 to 3 years before each survey because our focus is on child nutrition outcomes

which are especially crucial at these ages. The numbers of children in the sample in each

country and survey year are shown in Table 1. The earliest survey was conducted in 2001 and

the most recent was completed in 2010. For both Ghana and Nigeria, we pick only the regions or

states in the northern parts of the countries because they are the ones with the closest

resemblance to the Sahel environment. The final sample size is 55,772 observations.

Table 1: Sample sizes of children between 1-3 years, by country and survey

2001 2003 2005 2006 2008 2010 Total

Burkina Faso 0 5,733 0 0 0 8,421 14,154

Ghana 0 970 0 0 739 0 1,709

Mali 6,076 0 0 7,727 0 0 13,803

Nigeria 0 2,212 0 0 10,837 0 13,049

Senegal 0 0 5,498 0 0 7,559 13,057

Total 6,076 8,915 5,498 7,727 11,576 15,980 55,772

Source: Authors’s calculation using DHS, various years

Although maternal and child health are the primary concerns of the surveys, information

on household demographics, nutrition, asset holding, migration and employment is also

collected. Table 2 contains summary statistics for all variables used in the regressions. The top

part of the table shows the anthropometric Z-scores, and for rounds one and two surveys. Note

that the survey rounds are different from country to country, so that e.g. 2005 may be round 1 in

one country and round 2 in another. The second part of the table summarizes the shocks, the

construction of which is described in detail in the subsection that follows. The summary statistics

for some of the control variables which explain a large part of the variation in standards of living

are shown in the third section, while the bottom part shows the distribution of observations

among countries in the database. About 30 percent of the sample is from Northern Nigeria,

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while only 6 percent is from Northern Ghana. The share of the sample from the other three

countries is roughly the same – about 20 percent each.

Table 2: Summary statistics

Variable Observations Mean Std. Dev. Min Max

Height-for-age 32958 -119.36 182.99 -600 600

Weight-for-age 32958 -130.47 151.11 -592 600

Height-for-age (round 1) 13166 -115.61 174.75 -599 600

Height-for-age (round 2) 19792 -121.84 188.24 -600 600

Weight-for-age (round 1) 13166 -132.55 150.86 -592 600

Weight-for-age (round 2) 19792 -129.08 151.27 -591 599

Shock 54366 -.092 .217 -.79 .351

Rainfall levels (avg. mm/h) 62981 0.11 0.05 0.01 0.40

Urban 63911 0.26 0.44 0 1

Number of HH members 63911 8.73 5.96 1 74

Primary education 63911 0.17 0.37 0 1

Secondary education 63911 0.12 0.33 0 1

Higher education 63911 0.02 0.13 0 1

HH has toilet 63911 0.60 0.49 0 1

HH has electricity 63911 0.26 0.44 0 1

HH owns radio 63911 0.74 0.44 0 1

HH owns TV 63911 0.25 0.43 0 1

HH owns Refrigerator 63911 0.08 0.28 0 1

HH owns bicycle 63911 0.48 0.50 0 1

HH owns motorcycle 63911 0.25 0.43 0 1

HH owns car 63911 0.04 0.21 0 1

HH owns phone 63911 0.04 0.19 0 1

Dwelling has good floor 63911 0.44 0.50 0 1

Age of HH head 63777 43.29 13.66 13 97

Male headed HH 63911 0.89 0.31 0 1

Twin 63911 0.04 0.18 0 1

Male 63911 0.51 0.50 0 1

Burkina Faso 14154 0.22 0.42 0 1

Ghana 3874 0.06 0.24 0 1

Mali 13803 0.22 0.41 0 1

Nigeria 19023 0.30 0.46 0 1

Senegal 13057 0.20 0.40 0 1

Source: Authors’ calculation using DHS data (various years), TRMM and NDVI data.

3.2 Predicted Normalized Difference Vegetation Index as a measure of shocks

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The shock indicator is the predicted greenness, that is, our best estimate of the deviation

from long-run average of Normalized Difference Vegetation Index based on accumulated

anomalies in rainfall and temperature. Since it is a predicted anomaly, it is distributed around

zero when considering all years, but without fixed endpoints. The measure combines monthly

information on the NDVI, rainfall, temperatures at night and temperatures at daytime to predict

NDVI before aggregating to yearly averages.

Figure 5: Lagged monthly correlations between year-on-year changes in rainfall, temperature

and NDVI globally

Source: Authors’ calculation using NDVI and TRMM data.

For each DHS-cluster, monthly rainfall is estimated using the four nearest weather data

observations, as pictured in Figure 6. The estimated rainfall, NDVI, drought etc. in the cluster is

calculated as the weighted average of the measurement in these four points, which represent the

four corners of the world of the gridded cell to which the cluster belongs. The weights of the

equation are the inverse distances between the cluster and the weather observation, so that more

weight is assigned to data points, the nearer they are to the DHS cluster.

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Figure 6: Linking DHS clusters with gridded weather data

Figures 7 and 8 provide an overview of the key variables used for identification of

drought in the sample. The graphs show respectively the monthly and yearly variation in the

weather data employed. The growing season spans the summer months with average rainfall

peaking around a month earlier than greenness. Daytime temperatures drop during the rainy

season and again in the winter months before rising sharply throughout the dry season. Looking

at the graph of yearly variation in climate indicators, it is for instance noted that a drop in rain

and greenness in 2011 led to relatively dry conditions. Otherwise the period from 2006 to 2010

was characterized by relatively little variation in climatic conditions when observing the region

as a whole. But it is important to note that these averages may mask significant differences

between countries.

Figure 7: Monthly variation in weather data, all countries

14

Note: Z-scores (or anomalies – deviations from long run average)

Figure 8: Yearly variation in weather data, all countries

Note: Z-scores (or anomalies - deviations from long run average) are on the vertical axis. Shock is the predicted

greenness variable. Negative values correspond to relative drought, while positive correspond to relative green

conditions.

The levels of rainfall, greenness and daytime temperatures are shown in figure 9. As we

expected, there is a strong negative correlation between extreme temperatures and NDVI and

rainfall. Coastal Ghana and Nigeria are the wettest, the most green and coolest of the group,

whereas Sahelian Mali and Niger experience the driest and hottest conditions.

15

Figure 9: NDVI, rainfall and temperature; deviation from regional average by country

Note: Regional average=1. There is a strong negative relationship between rainfall/greenness and daytime

temperatures across the region.

To identify the impact of the shocks on child malnutrition, we link the child to the shock

that s/he experienced while in-utero. Since DHS collects information on the date of birth of each

child, we can calculate when a child was likely to be in-utero. We use that information together

with the knowledge of the spatial and temporal distribution of shocks to assign to a child the

most likely shocks that s/he faced. Therefore, this is in keeping with a large body of literature

that uses such natural experimental conditions to identify causal impacts (Neugebauer, Hoek,

Susser, 1999; Ravelli et al., 1998; Mu and Zhang, 2011; Stanner and Yudkin, 2001; Almond,

2006). Table A9 in the annex illustrates how growing periods and cohorts are matched.

4. EMPIRICAL MODEL

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Our main objective is to estimate vulnerability to child nutritional deficiency in the West

African Sahel. However, this involves several steps. The first step is to estimate the welfare costs

of shocks. Second, we need to obtain the probability distribution of the shock or the likelihood of

the shock that a household faces. Third, we need to estimate expected loss from a shock given its

average impact and probability distribution. Finally, we need to evaluate the resulting welfare

relative to a standard. We now describe how we obtain values we need in each of these steps.

Our first objective is to estimate household welfare losses from shocks. To identify the

impact of shocks on household outcomes, we exploit spatial and historical variation of shocks in

each location using a difference-in-difference approach. Although drought affects many

households at once, it tends to have strong spatial patterns. We will use spatial variation of

monthly historical rainfall recorded in the Sahel since 1998 to estimate the average impact of

rainfall on child nutrition. Droughts lead to large scale crop and livestock losses, which in turn

lead to high food prices and reduce access to food for many households. This is the first obvious

channel for the close link between drought and child nutrition. However, even when households

can protect the calories of children, they may do so by forgoing dietary diversity, which denies

children essential nutrients for their growth. We therefore estimate the following difference in

difference model.

�� = � + �� + � �� + �� +�� +�� +�� (1)

Where W is a welfare outcome (child malnutrition, measures of dietary diversity, consumption,

food security, and so on), and h, r, c, and t indexes household, region, country and time; S is the

shock, µ is a regional fixed effects, δ is a country fixed effects, θ is a time fixed effect and ε is a

household level error term.

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For identification, we use random year-to-year variation in exposure to shocks by adding

regional and country fixed effects. Such controls allow us to identify the impact of the shock

because they will be able to absorb the unobservable reasons why, on average, some locations

may have higher or lower child nutritional deficiency. One concern is whether our shock – which

relies on rainfall and temperature anomalies (see the data section above) is completely random.

We would be concerned in particular if households were to forecast the arrival of drought and

move away from the place most affected by it. However, while it is impossible for households to

forecast the rainfall risk of a location, it is possible that they can respond to drought incidence by

moving away for long periods of time. This will affect the composition of households, and if the

movers are households that are richer, for instance, then the estimates will be biased upwards.

Knowing the migration status of households will allow us to exclude migrants and run the

regression on non-migrants. To avoid spurious correlations between outcomes and drought

incidence, we introduce time fixed effects in all the models.

We run model (1) for all six countries in a pooled regression. Our coefficient of interest is

γ, which indicates the average impact of the shock on the welfare of interest: how much

nutritional deficits worsen, how much reduction in dietary diversity occurs, how much

consumption is foregone, and so on. We also run the model for subsamples of households: rural

versus urban, whether the head is male or female, and by education of the head of household.

Notice that an alternative equivalent specification is to run equation (1), but adding interaction of

these variables defining subsamples – urban/rural, female/male head, and education categories -

with the shock variable. The latter is the model we adopt.

The estimates in equation (1) above will provide us with the average impact of shocks on

outcomes. But in order to estimate the resulting welfare losses, we need to also obtain the

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probability distribution of shocks for each household. For each location, we can obtain the

distribution of shocks from historical data on drought incidence. In our case we have monthly

rainfall and temperature data for well-defined spatial grids from 1998 to 2012. Therefore we can

obtain the historical distribution of shocks that a household is likely to encounter at a location.

We exploit this knowledge in combination with our knowledge on the average effect of a shock

to calculate for each location the “expected loss” occurring from exposure to weather shocks for

each time period in our historical distribution of the drought measure. In some periods the

drought measure is positive, and the expected loss will be zero. Based on this, it is possible to

evaluate how many (if any) observations will fall below a specified outcome measure threshold

in a hypothetical future period, thus indicating vulnerability rates at different risk-levels.

We apply this empirical strategy to measuring vulnerability to malnutrition for young

children in West Africa. We use DHS data from five West African countries in order to capture

the incidence and prevalence of underweight and stunting that can be attributed to droughts. In

the next section we take up a discussion of the results.

5. RESULTS AND DISCUSSION

Table 3 shows the first stage results – the impact of shocks. The dependent variables are

the two most common measures of child malnutrition. Both variables are standardized relative to

the global reference median for children of the same age. The shock variable is also normalized

using the distribution over time and across space in our sample. The dependent variable is scaled

by 100. Therefore, the results suggest that for a standard deviation change in shocks, stunting

changes by around an eighth of a standard deviation. The impact on underweight is just slightly

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smaller, around a tenth of a standard deviation. These are average values across 6 countries and

control for time, country and province fixed effects.

In addition to the greenness index – which is used as the shock variable – we also control

for rainfall levels. Rainfall does not have any additional impact on stunting once the shock

variable is controlled for, but it does influence underweight. Plausibly, places with better rainfall

have better harvests and that is likely to lead to children with higher weights. Children are

comparatively healthier in richer households, as implied by those with TV, good floor, and have

more education.

Table 3: Impact of shocks on stunting (height/aage) and underweight (weight/age)

(1) (2)

ht/a standard deviations wt/a standard deviations

Shock 13.39*** 10.06**

Rainfall levels (avg. mm/h) 55.97 242.08***

HH has toilet 8.56*** 6.93***

Number of household members -0.95*** -0.75***

Primary education 15.92*** 14.87***

Secondary education 27.97*** 33.42***

Higher education 49.79*** 49.88***

HH has radio 4.29 3.19

HH has TV 15.61*** 14.91***

HH has refrigerator 13.43** 9.99**

HH has bicycle -5.72** -7.42***

HH has car 14.42** 11.53**

Dwelling has good floor 15.61*** 10.63***

Age of HH head 0.19** 0.10

Male headed HH -6.24* 2.08

Current age of child 9.19** 15.56***

Observations 31,995 31,995

R-squared 0.176 0.217

Robust standard errors in parentheses. Clustered standard errors in parentheses. Both columns

included interaction between shock variable and covariates as well as country, province and year

fixed effects.*** p<0.01 ** p<0.05 * p<0.1

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We now turn to our measure of vulnerability. Recall that to calculate vulnerability we

need the impact of shocks on welfare, and a probability distribution of a shock occurring. We use

the historical distribution of the predicted greenness index for each cluster and the average

impact obtained from the pooled regression to estimate the average welfare loss for individuals

living in each of the clusters. Our interest is to estimate the number of children who are

vulnerable to negative shocks within different probability intervals.

The steps required in order to achieve an estimate of the number of vulnerable children

are as follows: First, using the monthly distribution of our shock measure in the period 2000-

2012, we calculate the “expected anthropometric loss” in each month by multiplying the specific

value on the predicted greenness index by the coefficients on shocks obtained in the regression

above. The validity of this method rests on the assumption that the effect of a shock is linear in

values of the predicted greenness index. We also impose the assumption of “no positive gains”

(or alternatively no negative loss); meaning that all cases where the drought-measure is 0 or

above are seen as normal years and the expected loss is therefore set to equal 0.

Secondly, we follow the common practice in empirical vulnerability studies to calculate

the predicted value of our outcome measure for each child instead of using the actual values.

This is because vulnerability status is an ex-ante statement about a future scenario that has not

yet been revealed. Third, for each child we then proceed to calculating the share of months where

the predicted outcome measure (stunting or underweight) minus the expected loss lies below a

certain threshold. This share is a rough indication on the probability that a child will be

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stunted/underweight in a hypothetical future period. Based on this, we calculate the size of

different groups characterized by their risk of falling into malnutrition (50%, 25% 10% 5%).

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 face 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 4a: Vulnerability to stunting, Sahel West Africa.

Stunted Vulnerable

at 50 % risk

Vulnerable

at 25 % risk

Vulnerable

at 10 % risk

Vulnerable

at 5 % risk

Full sample 0.190 0.326 0.345 0.364 0.374

Burkina Faso 0.179 0.364 0.389 0.411 0.423

Ghana 0.245 0.301 0.333 0.371 0.387

Mali 0.237 0.269 0.293 0.321 0.334

Nigeria 0.286 0.527 0.540 0.548 0.553

Senegal 0.054 0.152 0.164 0.180 0.187

Rural 0.207 0.366 0.386 0.407 0.417

Urban 0.138 0.200 0.215 0.231 0.240

No primary education 0.196 0.342 0.361 0.381 0.390

Primary education 0.155 0.232 0.251 0.267 0.279

22

Female headed household 0.143 0.204 0.220 0.240 0.250

Male headed household 0.195 0.338 0.358 0.377 0.387

Table 4b: Vulnerability to underweight, Sahel West Africa.

underweight Vulnerable

at 50 % risk

Vulnerable

at 25 % risk

Vulnerable

at 10 % risk

Vulnerable

at 5 % risk

Full sample 0.197 0.355 0.380 0.404 0.415

Burkina Faso 0.219 0.494 0.518 0.541 0.549

Ghana 0.251 0.359 0.395 0.437 0.453

Mali 0.256 0.320 0.357 0.393 0.412

Nigeria 0.241 0.407 0.425 0.443 0.453

Senegal 0.058 0.182 0.198 0.214 0.220

Rural 0.216 0.406 0.432 0.457 0.468

Urban 0.138 0.195 0.216 0.239 0.248

No primary education 0.204 0.380 0.404 0.427 0.438

Primary education 0.155 0.211 0.239 0.266 0.277

Female headed household 0.149 0.260 0.284 0.305 0.315

Male headed household 0.202 0.365 0.390 0.414 0.425

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.

23

Figure 10: The cluster level vulnerability maps, height for age (left) and weight for age (right)

Source: produced using GADM v.2 (gadm.org) and GPS coordinates from DHS

6. CONCLUSION

In this paper we show that households in the West African Sahel experience multiple

shocks which lead to large welfare losses. We use a combination of household surveys and a

high resolution spatial and temporal measure of relative drought to estimate the average impact

of a shock on a child’s nutritional deficiency.

We find that on average a one standard deviation change in the shock leads to a change in

nutritional deficiency of between an eighth and a tenth of a standard deviation. However, these

welfare losses potentially hide large variations across individual countries in the sample. While

we see our results as a rough average of the effect, more rigorous (panel data) estimation

techniques could possibly yield a more precise indication of the true effect of weather shocks on

measures of malnutrition.

24

The study then used the estimated impacts of the shocks and the historical and spatial

distribution of shocks to calculate how many children are vulnerable to malnutrition under

different circumstances. We estimate that around a third of the children in our sample face a 50%

risk of falling into malnutrition in the near future, partly as a consequence of exposure to weather

shocks.

REFERENCES

Anttila-Hughes, Jesse and Solomon Hsiang. 2013. “Destruction, Disinvestment, and Death:

Economic and Human Losses Following Environmental Disaster.” UC, Berkeley.

Alderman, H., J. Hoddinott, and B. Kinsey. 2006. Long term consequences of early childhood

malnutrition." Oxford Economic Papers, 58(3): 450-474.Morduch, 1990;

Almond D. Is the 1918 influenza pandemic over? Long-term effects of in-utero influenza

exposure in the post-1940 U.S. population. Journal of Political Economy 2006; 114: 672-712.

doi: 10.1086/507154

Chaudhuri, S. 2001. “Assessing vulnerability to poverty: concepts, empirical methods and

illustrative examples.” Discussion paper series, Department of Economics, Columbia University.

Chen, Xi. 2014. “Fetus, fasting, and festival: the persistent effects of in-utero social shocks.”

International Journal of Health Policy Management, 3(4), 165–169

Christiaensen and Boisvert. 2000. “On Measuring Household Food Vulnerability: Case Evidence

From Northern Mali.” Working Papers 127676, Cornell University, Department of Applied

Economics and Management.

Christiaensen, Luc J. and Kalanidhi Subbarao, 2005. "Towards an Understanding of Household

Vulnerability in Rural Kenya," Journal of African Economies, Centre for the Study of African

Economies (CSAE), vol. 14(4), pages 520-558, December.

Dang, Hai-Anh and Peter Lanjouw. 2014. "Welfare dynamics measurement: two definitions of a

vulnerability line and their empirical application," Policy Research Working Paper Series 6944,

The World Bank.

Dercon, Stefan and Pramila Krishnan, 2003. "Risk Sharing and Public Transfers," Economic

Journal, Royal Economic Society, vol. 113(486), pages C86-C94, March.

Dercon, S., and L. Christiaensen. 2011. “Consumption risk, technology adoption and poverty

traps: evidence from Ethiopia." Journal of Development Economics, 96(2): 159-173.

25

Fisker, P. 2014. “Green Lights: quantifying the economic impact of drought”. IFRO working

paper series, University of Copenhagen.

Hill, R.V. 2009. “Using stated preferences and beliefs to identify the impact of risk on poor

households." The Journal of Development Studies, 45(2): 151-171.

Jacoby, Hanan G. and Emmanuel Skoufias. 1997. "Risk, Financial Markets, and Human Capital

in a Developing Country," Review of Economic Studies, Wiley Blackwell, vol. 64(3), pages

311-35, July.

Ligon, Ethan and Laura Schechter, 2003. "Measuring Vulnerability," Economic Journal, Royal

Economic Society, Royal Economic Society, vol. 113(486), pages C95-C102, March.

Mu R, Zhang X. Why does the Great Chinese Famine affect the male and female survivors

differently? Mortality selection versus son preference. Economics and Human Biology 2011; 9:

92-105. doi: 10.1016/j.ehb.2010.07.003

Neugebauer R, Hoek HW, Susser E. Prenatal exposure to wartime famine and development of

antisocial personality disorder in early adulthood. JAMA 1999; 281: 455-62. doi:

10.1097/00006254-200001000-00005

Pritchett, L., Asep Suryahadi, Sudarno Sumarto. 2000. Quantifying Vulnerability to Poverty: A

Proposed Measure, Applied to Indonesia. Policy Research Working Paper, World Bank.

http://dx.doi.org/10.1596/1813-9450-2437

Ravelli AC, van der Meulen JH, Michels RP, Osmond C, Barker DJ, Hales CN, et al. Glucose

tolerance in adults after prenatal exposure to famine. Lancet 1998; 351:173-7. doi:

10.1016/s0140-6736(97)07244-9

The Sahel precipitation index: doi:10.6069/H5MW2F2Q

Stanner SA, Yudkin JS. Fetal programming and the Leningrad Siege study. Twin Research 2001;

4: 287-92. doi:10.1375/1369052012498

Tiwari, Sailesh, Hanan G. Jacoby and Emmanuel Skoufias, 2013. "Monsoon babies : rainfall

shocks and child nutrition in Nepal," Policy Research Working Paper Series 6395, The World

Bank.

Townsend, R.M. 1994. Risk and Insurance in Village India." Econometrica: Journal of the

Econometric Society, 62(3): 539{591.Karlan et al. 2013;

Udry, C. 1994. “Risk and insurance in a rural credit market: An empirical investigation in

northern Nigeria." The Review of Economic Studies, 61(3): 495-526.

26

ANNEX 1: FIGURES AND TABLES

Figure A1: Rainfall and prices, Burkina Faso and Ghana

Source: Authors’ calculation using VAM and TRMM data

Figure A2: Rainfall and prices, Mali and Niger

50

100

150

200

250

Avera

ge n

om

inal price

(XO

F/K

G)

0100

200

300

Rain

fall(

mm

)

2000

m7

2001

m7

2002

m7

2003

m7

2004

m7

2005

m7

2006

m7

2007

m7

2008

m7

2009

m7

2010

m7

2011

m7

2012

m7

Rainy season Dry season

Maize Millet

Sorghum

Burkina Faso

050

100

150

200

Ave

rage n

om

inal price(G

HS/u

nit*

)

050

100

150

200

250

Rain

fall(

mm

)

2008

m7

2009

m7

2010

m7

2011

m7

2012

m7


Cassava Maize

Plantain_Apentu Rice_Local

Yam

Ghana

100

200

300

400

500


050

100

150

Rainfall(mm)

2003m7

2004m7

2005m7

2006m7

2007m7

2008m7

2009m7

2010m7

2011m7

2012m7


Maize Millet

Rice_Imported Rice_Local

Sorghum

Mali

100

200

300

400

500

600


020

40

60

80

100

Rainfall(mm)

2000m7

2001m7

2002m7

2003m7

2004m7

2005m7

2006m7

2007m7

2008m7

2009m7

2010m7

2011m7

2012m7


Beans Maize

Millet Rice_Imported

Sorghum

Niger

27


Figure A3: Rainfall and prices, Nigeria (North) and Senegal


Figure A4: Rainfall and prices, Burkina Faso and Ghana (rainy season months)


50

100

150

200

250

300

Ave

rage nominal price

(XOF/K

G)

0100

200

300

Rainfall(mm)

2002m

7

2003m

7

2004m

7

2005m

7

2006m

7

2007m

7

2008m

7

2009m

7

2010m

7

2011m

7

2012m

7


Maize Millet

Sorghum

Nigeria

100

200

300

400

Ave

rage nominal price

(XOF/K

G)

0100

200

300

Rainfall(mm)

200

0m7

200

1m7

200

2m7

200

3m7

200

4m7

200

5m7

200

6m7

200

7m7

200

8m7

200

9m7

201

0m7

201

1m7

201

2m7


Maize Maize_Imported


Sorghum

Senegal

50

100

150

200

250

Avera

ge n

om

inal price(X

OF/K

G)

100

150

200

250

300

Rain

fall(m

m)

2000

m7

2001

m7

2002

m7

2003

m7

2004

m7

2005

m7

2006

m7

2007

m7

2008

m7

2009

m7

2010

m7

2011

m7

2012

m7


Millet Sorghum

Burkina Faso

050

100

150

200

Avera

ge n

om

inal price

(GHS/u

nit*

)

100

150

200

250

Rain

fall(m

m)

2008

m7

2009

m7

2010

m7

2011

m7

2012

m7

Average monthly rainfall Cassava

Maize Plantain_Apentu

Rice_Local Yam

Ghana

28

Figure A5: Rainfall and prices, Mali and Niger (rainy season months)


Figure A6: Rainfall and prices, Nigeria and Senegal (rainy season months)

100

200

300

400

500

Avera

ge n

om

inal price(X

OF/K

G)

050

100

150

Rain

fall(

mm

)

2003

m7

2004

m7

2005

m7

2006

m7

2007

m7

2008

m7

2009

m7

2010

m7

2011

m7

2012

m7



Rice_Local Sorghum

Mali

100

200

300

400

500

600

Avera

ge n

om

inal price(X

OF/K

G)

020

40

60

80

100

Rain

fall(

mm

)

2000

m7

2001

m7

2002

m7

2003

m7

2004

m7

2005

m7

2006

m7

2007

m7

2008

m7

2009

m7

2010

m7

2011

m7

2012

m7

Average monthly rainfall Beans

Maize Millet

Rice_Imported Sorghum

Niger

50

100

150

200

250

300

Ave

rage nominal price(X

OF/K

G)

100

150

200

250

300

Rainfall(m

m)

2002

m7

2003

m7

2004

m7

2005

m7

2006

m7

2007

m7

2008

m7

2009

m7

2010

m7

2011

m7

2012

m7


Millet Sorghum

Nigeria100

200

300

400

Ave

rage nominal price

(XOF/K

G)

50

100

150

200

250

300

Rainfall(mm)

2000

m7

2001

m7

2002

m7

2003

m7

2004

m7

2005

m7

2006

m7

2007

m7

2008

m7

2009

m7

2010

m7

2011

m7

2012

m7


Maize_Imported Millet

Rice_Imported Sorghum

Senegal

29


Figure A7: Reported shocks from latest household surveys

Source: Authors’ calculation using recent household surveys, various.

Figure A8: Calculation of NDVI using Infrared radiation from satellite data.

Source: NASA (earthobservatory.nasa.gov)

1.25.9

9.213.6

30.743.1

65.372.4

0 20 40 60 80Share of households (%)

OtherIncome shock

Death of HH memberCrime and conflict

Loss of assetsIllness, accident

Natural/weather shocksPrice shock

Burkina Faso

3.9

9.9

12.6

12.7

18.1

20.9

27.0

0 10 20 30Share of households (%)

Income shockPrice shock

Death of HH memberIllness, accident

Loss of assetsOther

Natural/weather shocks

Mali

2.76.46.5

7.512.7

17.228.3

30.9


Crime and conflictDeath of HH member

Illness, accidentIncome shockLoss of assets

OtherNatural/weather shocks

Price shock

Niger

2.53.3

4.26.2

7.99.6

10.512.0


OtherCrime and conflict

Loss of assetsIllness, accident

Price shockDeath of HH member

Natural/weather shocksIncome shock

Nigeria

0.9

5.0

5.7

11.5

15.3


Loss of assets

Death of HH member

Income shock

Illness, accident

Natural/weather shocks

Senegal

30

Table A9: Which growing seasons are included in shock-measure for different cohorts?

Born last year Born 2 years ago Born 3 years ago

Shock year t-1 X

Shock year t-2 X

Shock year t-3 X

Annex 2: Description of NDVI data

The images used in this analysis are so-called monthly maximum value composites.

Since all atmospheric influence lowers NDVI, NASA stores only the highest greenness-value for

each pixel over the period, where most pixels are recorded daily. This way cloud cover is filtered

out in almost all cases. It is not straightforward to use NDVI as a proxy for drought, however.

Year-on-year variation in greenness might be caused by factors other than climatic changes. As

an example, deforestation quickly reduces the greenness of an area without being associated with

drought. On the contrary, deforestation is often a sign of increased economic activity in a region.

Broadly speaking, all factors that are non-climatic but affect the greenness of the planet will

create noise in the picture of NDVI anomalies as a drought indicator. Most of these factors

would be anthropogenic and, apart from deforestation, include changes in cultivation, irrigation

and urban expansion. We use predicted NDVI, which takes greenness into consideration, but

importantly leaves out all anthropogenic causes of change in “greenness”. The details of the

construction of the index can be found in Fisker (2014).

31

From space it is possible to observe the surface of the earth and measure the light that is

emitted at different wavelengths. Vegetation indexes such as the NDVI translate visible red and

near infrared radiation into a decimal number between -1 and 1 which describes the greenness of

a specified geographical area. In order to use NDVI as a proxy for drought, it is common to

calculate the anomaly, i.e. the deviation from a long-run average for a specific time of the year.

Figure A8 shows how NDVI is calculated as the ratio between near infrared radiation and visible

red radiation; a higher index value is related to a greener land surface.

NDVI data is obtained from the MODIS Terra satellite. It has been orbiting Earth daily

since 2000, and here we employ a pre-processed product made publicly available by NASA that

has a temporal resolution of one month and a spatial resolution of 0.05 degrees (3 arc minutes or

around 5.8 km at the equator). It is later aggregated to 0.25 degrees in order to match the

resolution of the rainfall data and reduce the number of observations. In the end we have a data

frame with 1440 x 720 observations over 180 months for every location.

Like NDVI, land surface temperature is measured from space globally using the MODIS

Terra satellite, and again, the product in use has a spatial resolution of 0.05 degrees. Year-on-

year changes in both daytime and night time temperatures are included in the model (see Table

A2 in the Annex). On average, it is expected that day time temperatures affect greenness

negatively since hotter means drier in most parts of the world. Night time temperatures are likely

to affect greenness positively, however, since cold also becomes a serious constraint for plant

growth when moving away from the equator.

While greenness is best seen from above, rainfall is harder to measure using satellites.

This study uses data from the Tropical Rainfall Measuring Mission (TRMM) which to our

32

knowledge is the most precise and valid remote sensing estimate of rainfall for the relevant

period. In terms of spatial extent and resolution, the TRMM data is not as good as our measures

of greenness and land surface temperature. It includes pixels of 0.25 degrees, which seems

sufficient for our purpose.

The link between year-on-year change in NDVI and the climatic background variables

for every month is modeled using up to 11 lags so that it is only what has happened during the

preceding year that is included. The technical aspects regarding the estimation of predicted

greenness is described in Fisker (2014).

Table A2.1: Predicting NDVI using rainfall and temperatures

vulnerability to malnutrition in the west african...

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