Safety nets or investment in the future: Does food aid have any long-term impact on children's growth?

Journal of International Development

J. Int. Dev. 22, 1134–1145 (2010)

Published online in Wiley Online Library

(wileyonlinelibrary.com) DOI: 10.1002/jid.1751

SAFETY NETS OR INVESTMENT IN THEFUTURE: DOES FOOD AID HAVE

ANY LONG-TERM IMPACT ONCHILDREN’S GROWTH?

CATHERINE PORTER*

Department of Economics, University of Oxford, Oxford, UK

Abstract: This article investigates child nutrition in rural Ethiopia, examining anthropo-

metric information collected on children aged below 5 years in 1995, and again almost

10 years later for the now young adults in 2004. It finds that children who were malnourished in

their early years are far more likely to be stunted 10 years later on. The article examines the

role that food aid plays in mitigating the impact of adverse events and finds that it does have a

lasting effect and could have even more impact if well targeted and delivered. The findings

underline the importance of investing in early childhood nutrition and tailoring public

programmes to meet the needs of children. Copyright # 2010 John Wiley & Sons, Ltd.

Keywords: Ethiopia; nutrition; children

1 INTRODUCTION

Risk, unexpected adverse events, seasonal deprivation and crises are an everyday fact of

life for many poor households, not least in developing countries. Poor men, women and

children have found many creative solutions to protect themselves and their households

from the adverse consequences of such events and to maintain their food consumption

above subsistence levels as much as possible. Poorer people tend to be exposed to more

risk, and also have fewer resources to deal with adverse events when they materialise.

Many developing countries do not have formal social protection systems that are part of

the social contract. Rather, when widespread crises occur, ad hoc and often external

interventions (government or external donors or a combination of both) are needed in order

to provide short-term support to the hungry. In addition there are households or individuals

*Correspondence to: Catherine Porter, Department of Economics, University of Oxford, Manor Road, OxfordOX1 3UQ, UK. E-mail: [email protected]

Copyright # 2010 John Wiley & Sons, Ltd.

Safety nets or Investment in the Future 1135

who are in a situation of chronic poverty, due to low assets, social exclusion, ill health or

disability, and who also rely on what informal or public support is available to them.

In Ethiopia, this is certainly the case, and in 2009 the Government of Ethiopia estimated

that 6.2 million people were in need of food aid.1 Very young children are particularly

vulnerable to the consequences of malnutrition, showing higher mortality than older children

and adults. Images of children are frequently used to garner support for emergency food-aid

programmes in rich donor countries, and yet relatively little is known about the longer-term

impacts of emergency aid on children due to the lack of high-quality data in developing

countries, especially those that are vulnerable to food crises and famines. Specifically, there

are very few surveys that follow the same children over time, in order to assess such impacts.

This study examines the nutritional status of children born in Ethiopia the early 1990s,

followed through 10 years up to 2004. We examine the persistence of stunting throughout

childhood, and also the impacts of food aid distribution in the medium term. The sample

also offers a comparison group for the children who are the first to be covered by the

Productive Safety Net Programme (PSNP), a more comprehensive national safety net that

was introduced in 2004.

The focus of our study is those who were in early childhood (defined as up to the age of

5 years) at the start of the survey period. We wished to examine over as long a period as

possible how the physical development of children exposed to certain adverse events in

early childhood progressed, comparing children from households who are all poor relative

to international standards, but with a degree of inequality even within the sample. We

compare more and less poor children, those who had better or worse nutritional status and

members of households that did and did not receive food aid. We do not have information

on food consumption of each individual in any given household, so we cannot directly

observe nutritional intakes. Instead, we infer nutritional outcomes by observing children’s

anthropometric status (height and weight) in the early years, and their subsequent growth

over the 10-year period.

2 OTHER EVIDENCE ON EARLY CHILDHOOD DEVELOPMENT IN THE

CONTEXT OF CRISIS

This section outlines where this article fits into a small but growing literature that uses

quantitative evidence to document the importance of early childhood nutrition, in

particular, those linking early childhood conditions with later-life outcomes. There is an

established consensus that the first 5 years of life are extremely important, and that adverse

events experienced in this time may even have permanent effects. Literature in medicine,

biology, economics, developmental psychology and sociology has documented the

importance of the early childhood years in terms of life-chances as measured by outcomes,

such as adult height, illness and earnings, mostly in the developed world. Barker (1992)

documents the foetal and early childhood origins of illness experienced in adulthood.

Almond (2006) in the economics literature finds a significant impact of the 1918 US

influenza epidemic on adult height of a cohort of children as yet unborn but in the womb at

the time of the pandemic. Grantham-McGregor et al. (2007) provide a multidimensional

overview of the evidence for a number of developing countries in Africa, Asia and

Latin America, and show that poor nourishment in early childhood inhibits cognitive

1Ethiopian Disaster Prevention and Preparedness Committee. See http://www.dppc.gov.et/ [accessed 8th January2010].

Copyright # 2010 John Wiley & Sons, Ltd. J. Int. Dev. 22, 1134–1145 (2010)

DOI: 10.1002/jid

1136 C. Porter

development, leading to poor schooling outcomes and worse health in adulthood.

Alderman et al. (2006) show that drought and civil war in Zimbabwe affected children’s

height several years later. Dercon and Porter (2009) show, using the same data on Ethiopia

used by this study, that very young children during the 1984 Ethiopian famine grew up to be

around 3 cm shorter than their unaffected peers, 20 years on from the crisis. On a more

positive note, a small body of evidence is available on the longer-term impacts of a

nutritional intervention in Guatamala, see for example Maluccio et al. (2009).

For a comprehensive review of the economics literature on health over the life-course,

see Strauss and Thomas (2008). Cunha et al. (2006) outline a formal economic model that

incorporates certain key empirical insights into child development, and is the theoretical

underpinning of this paper. In particular, the authors suggest that there are certain

development processes in very early childhood that if not achieved may be irreversible:

children who miss out on these developments may well never catch up. Secondly, the

authors outline the possibility of complementarities between different dimensions of child

development, for example between psycho-social skills and cognitive development. This

builds on earlier economic models based on the ‘health production function’ Grossman

(1972) that essentially try to simplify the complex biological and sociological process of

child development into a set of inputs (such as the amount and quality of food, exposure to

germs, quality of drinking water) and link those to health outcomes, such as incidence of

disease or height, as cumulative measure of nutrition.

3 FOOD AID, FAMINE AND DROUGHT RELIEF IN ETHIOPIA

Ethiopia’s climate is notoriously dry with erratic rainfall, and there have been several

severe famines in living memory, most notably in 1974 and 1984, though many also before

that (Pankhurst, 1986). In 1994–1995 at the time of the original study, food aid was

organised into free distribution (FD) and food-for-work (FFW) programmes.

Clay et al. (1999) show that there were considerable targeting errors in the distribution of

food aid at this time. Their study, based on 1996 data, finds no significant association

between household food insecurity (vulnerability) and receipt of food aid. Jayne et al.

(2001) find large differences in food aid allocations across regions that cannot be explained

by observable regional characteristics such as per capita income and rainfall. They also find

wide variations in the criteria used to identify recipient households across regions. Similar

findings from the same data show that inertia of the authorities in changing the location of

food aid distribution, due possibly to fixed costs (such as the set-up of a village distribution

station or recruitment of local administrators) led to food allocations that did not

necessarily reach the neediest—if a village had received food aid in the past then it would

be more likely to be a current recipient (Jayne et al., 2002).

Other studies on the impact of childhood nutrition in Ethiopia include Yamano et al.

(2005) and Quisumbing (2003). Quisumbing (2003) uses the Ethiopian Rural Household

Survey (ERHS) dataset up to 1997 to examine the effects of FD and FFW on child

nutritional status. These differ depending on whether the food aid was delivered free or via

FFW, and the sex of the child. She finds that both FFW and FD have a positive direct impact

on weight for height. Households that received FD showed improvements in girls’

nutrition, while for those receiving food from FFW nutrition improved for boys.

In 2005, the Government of Ethiopia introduced a more comprehensive, predictable and

transparent safety net scheme—the PSNP. The PSNP still includes a work component and


DOI: 10.1002/jid


free food/cash distribution at the core of the programme, with supposedly more predictable

aid every year, a greater focus on asset building, and a further component meant to improve

the prospects for households to ‘graduate’ from the scheme.2 This is known as the Other

Food Security Programme (Government of Ethiopia, 2004). The PSNP has also moved

away from food distribution to a mixture of cash and food (sometimes for the same

household). In 2008, only approximately 45 per cent of the programme’s transfers were in

food, 55 per cent in cash. This marks a significant departure in donor policies, although

dramatic increases in food prices have reduced the purchasing power of those receiving

cash and many households have said they would prefer food to guarantee their

consumption levels (Hobson, 2009).

A small number of recent studies have examined the effect of the PSNP on children’s

welfare outcomes in the short-term. Hoddinott et al. (2009) use the official PSNP

evaluation data to examine child schooling and time use. Using the Young Lives dataset,3

Woldehanna (2009) examines the impact of the PSNP on children’s time use and schooling,

as does Orkin (2010) including qualitative research in one particular village. The findings

of all three papers indicate that whilst the safety net may be adding income to a household,

the work requirement can lead to increase in children’s working hours. Future work on the

Young Lives data will be able to assess whether the impacts of food aid are significantly

improved following the reform of the programmes compared to this study.

4 DATA

We use information from the ERHS, collected by the University of Addis Ababa with the

University of Oxford and the International Food Policy Research Institute (IFPRI). The

dataset spans 10 years from 1994 to 2004. The ERHS is small (covering 15 villages), but

representative of the main agricultural systems of the country (see Dercon and Krishnan

(1998) for more details on the sampling process and background to the villages). The

survey collected information on household assets, consumption and income as well as risk and

adverse events experienced by the household, and the education and health of individual

members as well as anthropometric measurements. Just under 1500 households were surveyed

in the first round, and attrition has been relatively low at the household level, at around three per

cent per annum. There is more attrition at the individual level which we discuss below.

The focus of our study is those who were in early childhood at the start of the survey in

1994. We have a sample of 468 children from 387 households who were aged under 5 years

in 1995, the third round of the survey.4 We have also have information on their height and

weight 10 years later in the sixth round, 2004, as well as their grade in school. To measure

the stock of nutritional achievement of the children, we use the height-for-age z-score

(HAZ).5 HAZ are recommended by the World Health Organisation (WHO) as a measure of

child development as they are a good indicator of the cumulative investments in child

2See Pankhurst (2009) for a discussion on the complex issues surrounding graduation and vulnerability in theEthiopian context.3Young Lives is a four-country longitudinal study of childhood poverty over 15 years in Ethiopia as well as Peru,India and Vietnam.(www.younglives.org.uk)4We could have based the sample on those aged under 60 months in Round 1, but in the analysis we can utiliselagged data (i.e. from the first two rounds) in order to circumvent some econometric concerns. Hence, for thisreason, the youngest child for which we have information in Round 1 is 11 months in 1995.5Raw data for the comparison groups available at: http://www.who.int/childgrowth/standards/height_for_age/en/index.html.


DOI: 10.1002/jid

Table 1. Descriptive statistics

Mean Standard deviation

Height (cm) 1995 86.829 11.298

HAZ 1995 �2.450 1.686

Height 2004 135.068 11.956

HAZ 2004 �2.274 1.451

Real aid per capita (1994 Birr) 13.277 56.118

Real consumption per cap (ex aid) 76.309 75.640

Relative rainfall (1–5) 2.968 1.351

Nr ill in household 0.669 1.129

Agricultural loss �0.230 0.206

Number female adults 1.632 1.045

Number female 5–15 1.137 0.993

Number female under 5 0.725 0.787

Number female elderly 0.139 0.361

Number male 5–15 1.151 1.104

Number male under 5 0.828 0.785

Number male elderly 0.084 0.285

Sex (female¼ 2) 1.445 0.497

Sex of HH head (female¼ 0) 0.836 0.370

Livestock units 3.051 4.069

Rainfall perception index 0.596 0.353

Public works in 2002 0.347 0.476

Food aid in 2002 0.234 0.423

Number of observations per round 436

1138 C. Porter

nutrition. They show the height of the child relative to a reference group of healthy children

of exactly the same age. This allows for comparison with other empirical studies. We

calculated height-for-age and weight-for-age z-statistics (WAZ) using the latest WHO

standards.6 A HAZ score of zero means that a child has the average height for their age,

where the average is calculated using the healthy comparison group. A negative HAZ

(or WAZ) means that the child is below the average height (weight) for a healthy child of

the same age and sex. A HAZ (WAZ) of below two is defined as stunted (or underweight),

and below three as severely stunted (or underweight). With an average HAZ of �2.45 for

the whole sample in 1995, this sample overall is stunted compared to the healthy

international comparison group. Otherwise stated, the z-score of �2 means that 97.7 per

cent of healthy children are taller than this, thus the median height of a 3-year old boy

in the reference group would be 100 cm (HAZ of 0.0) while a boy of the same age with a

z-score of �2.0 is 11 cm shorter. Table 1 shows descriptive statistics for the sample used in

the analysis.

6WHO has recently compiled a new dataset for under-5s based on a broader reference group of healthy childrenfrom seven countries, two of which were developing countries (the WHO Multicentre Growth Reference Study,MGRS) (see de Onis et al., 2007). The new reference group is selected from healthy, breastfed children in all sevencountries, and is meant to indicate a more prescriptive approach (e.g. how healthy children should grow—theunconstrained pattern). In the same symposium that created the new dataset for under 5s in 2006 recommendationswere made to introduce new references for older children, particularly with regard to the shortcomings regardingweight/BMI indicators (body mass index¼weight in kilograms squared, divided by height in centimeters). Thelatest data are based on a WHO recalibration of the original American (NCHS) reference group for children over 5.


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5 DISCUSSION OF METHODOLOGY

Our concern is to quantitatively estimate the nutritional status of children in 2004,

examining whether undernutrition in their early childhood has caused a slowing of long-

term growth, or whether the children have caught up with their peers in the intervening

decade. Further, we are interested in examining whether food aid has played any long-term

role in child growth.

We draw on the approach outlined in Section 2 above (e.g. Alderman et al., 2006) for our

empirical specification, and outline a model that specifies height, Hit as a proxy for long-

term nutritional status, as a function of child characteristics Xi, (such as sex, birth order

and genetic characteristics), household characteristics Xh (wealth, nutritional availability,

composition of the household, characteristics of parents) and community characteristics Xc

(rainfall, general nutrition availability). Height can be thought of as a ‘stock’ of nutrition

(de Onis et al., 2007) and, therefore, we would appropriately include the lagged levels of

height in a model.

Hit ¼ f ðHit�1;Xi;Xh;Xc; uÞ (1)

Included in the household characteristics would also be receipt of food aid. Ideally, we

would like to know how much food aid was allocated directly to each child, but we only

have household-level information. Even when food aid has been allocated to the most

needy households, its effectiveness also depends on how it is distributed among household

members.

Our empirical specification is then

Hit ¼ aþ b1Hit�1 þ g i þX

bjXit þX

bkXht þX

bsXct þ eit (2)

where Xit captures time-varying child characteristics Xht are household time-

varying characteristics and Xct at the community level (we also control for community

fixed effects). The main econometric concern is that time-invariant variables g i that

are unobservable to us may be correlated with both the ‘current’ (2004) and previous

(1995) height estimates, for example, the household’s general approach to child

nutrition, or genetic factors. Since height is measured by enumerators, we assume that

any measurement error is likely to be random, though our empirical strategy does help to

deal with this problem. We take the difference in height over the two periods in order to

ensure that our results are not correlated with any unobserved factors about the child or

household that do not change over time (the fixed effect) that would cause us to spuriously

overestimate the causal effect of nutrition in early childhood on future height, and use an

instrumental variables strategy (First-Difference Generalised Method Of Moments, GMM)

where we include twice-lagged height (e.g. height in the first period of 1994) as the

instrument for height in 1995. We report the appropriate specification tests in the results

section, in order to confirm that our strategy is suitable. The assumption necessary for

consistency of the estimates is that there is no serial correlation in time varying but

unobserved characteristics such as innate healthiness. We acknowledge that this is a strong

assumption. For a more technical discussion of this problem, see Blundell and Bond (1998)

or Wooldridge (2001).


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Table 2. Standard poverty measures, by round

Year Headcount Poverty gap Sq-poverty gap Mean consumptionamongst poor (Birr)

1994 0.39 0.16 0.08 26.80

1995 0.44 0.19 0.10 25.70

2004 0.23 0.07 0.04 30.26

Source: ERHS data, poverty line is 44.3 Birr per adult (1994 prices), per month

1140 C. Porter

6 ANALYSIS OF NUTRITIONAL STATUS IN 1995

Ethiopia is a very poor country, and poverty as measured by a consumption deficit remains

pervasive, especially in rural areas. Table 2 shows poverty statistics for the full sample in

1994, 1995 and 2004 of the ERHS survey.

In 1994 at the start of the survey, 39 per cent of households are below the poverty line.

We use a very low poverty line (at around 30 US cents per day), to be comparable with other

studies using the ERHS data. Dercon and Krishnan (1998) calculated this as the amount

needed to reach food consumption of 2000 calories per day (and proportionally lower for

women and children), plus a very small amount of non-food expenditure on fuel. We can

see that poverty falls by all measures over the 10-year period, though in 2004 there are still

more than a quarter of all households in poverty.

In Table 3, we focus on 1995, the baseline for nutritional status in our analysis.

Comparing children from households that were below the poverty line, we see that their

height is unsurprisingly lower than those above poverty it (and remember that this is an

extremely austere poverty line). We find that the overall HAZ is significantly lower (at 5 per

cent using a t-test) for children living in poor households rather households above the

poverty line. The proportion of stunted children in both groups is not significantly different,

but the proportion of very stunted children higher for the poor. Even for relatively better-off

households, the proportion of very stunted children is high at 31 per cent. We find no

significant differences in height for age by sex, confirming other studies of rural Ethiopia

(e.g. Hoddinott et al., 2009). A similar pattern applies for weight-for-age, with non-poor

children significantly heavier than the poor children.

Applying the same tests (i.e. on poverty status in round 1) to height for age in 2004, we

see no significant differences later on in life (aged 10–15 years). We note, however, that to

be included in our sample in 2004 the child must have survived into that year. There are also

other reasons that children are not followed through to the 2004 survey (for example,

households moving, lost roster cards) so we examine the differences between those who are

still in our sample and those who do not in the robustness checks.

Table 3. Nutritional status of poor and non-poor children under five (in 1995)

Poor Mean (HAZ) Mean (WAZ) Mean (stunted) Mean (V.stunted)

Non-poor �2.319 �1.461 0.542 0.314

Poor �2.656�� 1.910�� 0.537 0.388�

Notes: Poor is defined in the text and Tables 1 and 2 as consumption below 44.3 birr per month.�Significant at 90 per cent;��Significant at 95 per cent.


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We can analyse the determinants of height-for-age in 1995 based on some characteristics of

the child, including age, sex, birth order and recent illness, as well as household

characteristics, including composition of the household, assets, receipt of food aid and any

adverse events that may have occurred. Measures of the adverse events include rainfall (which

may also be better than expected), a self-reported agricultural loss index comprising eight

potential adverse events that may happen to crops such as frost, animal trampling, insects and

crop-diseases. Also we include illness within the household. We use real consumption per

adult equivalent, and the same for the receipt of food aid (with 1994 as the base year).

We are interested in whether nutritional status in 1995 predicts that of 2004—in other words

whether there is persistence in nutritional outcomes. If there is, it indicates the difficulty of over-

coming poverty in rural Ethiopia, and that nutritional interventions for under-5s could have a lasting

impact in breaking the inter-generational cycle of poverty. Similarly, preventing severe nutritional

deprivation in early life or mitigating the nutritional impacts of adverse events in a timely

fashion can have lasting benefits beyond the already important alleviation of suffering in a crisis.

Table 4 shows the relationship between stunting in 1995 and stunting in 2004. We

see that in 1995, 45 per cent of under-5s are stunted, and in 2004 the same percentage

are stunted. Whilst these are not all the same children, we can observe that children who

are stunted when under age 5 are twice as likely to be stunted in early adolescence. The

same is true for those who are severely stunted. The correlation between being stunted in

both periods is 0.19 (significant at 1 per cent). For this sample, the probability of being

stunted in early adolescence is twice as high for those who were stunted in early childhood.

Only 27 per cent of the sample children are not stunted in either period, However, more than a

third (36 per cent) are stunted in both. Of those stunted in 1995, just under 37 per cent are severely

stunted in 2004, which means that the majority of stunted children did experience at least some

catch-up in the 10-year period. Severe stunting is a much more serious issue both for its own sake and

the increase in probability of future stunting that it implies. Of those children who were severely

stunted in 1995 (for a 3-year old boy that means being almost 15 cm shorter than the average for

a healthy comparison group), over 70 per cent are stunted in 2004. We see then that stunting in

the early years is a strong predictor of stunting in early adolescence, in particular severe stunting.

In order to understand better who are the worst affected children, we profile in Table 5

the characteristics of the children who are stunted in 1995. The table shows that girls and

boys are equally likely to be stunted whether the household is headed by a woman or by a

man. Similarly, being from a large or a small household makes no difference to the

likelihood of being stunted. However, children who are stunted are much more likely to

come from poorer households (proxied here by assets as scaled livestock units),7 and those

Table 4. Stunting in 1995 and 2004

Stunted 2004

Stunted 1995 No (%) Yes (%) Total (%)

No 27.56 18.38 45.94

Yes 17.74 36.32 54.06

Total 45.30 54.70 100.00

Notes: Stunting defined as HAZ<�2, as described in text.

7The scaled livestock units are a standardised measurement—so that we can roughly compare different animals.The standard measure has been compiled by the Food and Agricultural Organisation (FAO) of the UN.


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Table 5. Characteristics of children, by nutritional status

Non-stunted Stunted Total

Height-for-age �0.71 �3.50� �2.45

Weight-for-age �1.12 �2.05� �1.64

Age in months 39.01 37.97� 38.45

Sex (female¼ 2) 0.56 0.55 0.56

Household size 7.24 7.24 7.24

Sex of HH Head (female¼ 0) 0.86 0.85 0.86

Livestock units 3.20 2.57� 2.86

Real consumption/adult (exc. Food aid) 68.11 67.80 67.94

Real food aid/adult 2.34 0.58� 1.39

Agricultural loss �0.18 �0.18 �0.18

Nr ill in household 0.38 0.48 0.44

Notes: Real consumption and food aid have been deflated to 1994 Ethiopian Birr. No causal links assumed.�Significant at 90 per cent.

1142 C. Porter

that receive less food aid. Including food aid, we test whether total consumption is different

between both groups, and find no differences.

We are interested in two main questions. Firstly, is there any catch-up in growth over the

10-year period, or are children who are short for their age or sex always going to remain so?

Second, is there any impact of food aid on subsequent growth? As noted in the econometric

strategy, we can estimate growth between 1995 and 2004 using methods that do not

spuriously pick up ‘third factors’ unobserved to us and attribute them to causal effects of

undernutrition in childhood. We also use lagged values of food aid received for the same

reasons—there could be something unobservable about the household (or child) that is

correlated both with the amount of food aid received and with height. This strategy is also

useful when measurement error is present. We use the methodology outlined in Section 5

above and show the results in Table 6.

We would expect a negative coefficient on the lagged height-for-age coefficient if there is

any catch-up (i.e. the shorter children grow faster)—a coefficient of�1 would mean complete

catch-up. In this case the coefficient is relatively low, which indicates quite limited levels of

catch-up. The amount of food aid received in the ‘snapshot’ we have when the child was under

5 years appears to be very significant. We do not have enough data to ascertain whether there

are differential impacts for boys or girls, though Quisumbing (2003) does find some which

relate to the method of distribution between 1994 and 1997. Some questions were asked in the

sixth round (2004) survey about experiences during the 2002 drought. We, therefore, include a

dummy variable indicating whether the household received food aid free, or through

participation in public works during 2002. Participation in public works is not significant

for our sample, and food aid is positively related to growth (though our estimates of this are

slightly less precise—a p-value of 0.12, or significant only at 88 per cent).

7 ROBUSTNESS CHECKS

Because we used an instrumental variables strategy, we run the risk that our estimates will

be less consistent and possibly biased compared to the standard Ordinary Least Squares

(OLS). To test for weak instruments (that the instrument we use does not have enough

explanatory power to accurately predict a value), we present in Table 5 above the


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Table 6. Determinants of nutritional status in 2004

Change in HAZ

HAZ 1995 �0.138��

(0.047)

Food aid received in 1995 0.008��

(0.002)

Consumption (exc. aid) 0.002�

(0.001)

Relative rainfall 0.016

(0.241)

Rel. rain (1 year ago) 0.401��

(0.147)

Sex �0.142

(0.126)

Public works 2002 0.097

(0.188)

Food aid 2002 0.247

(0.162)

Constant �1.593

�1.174

Observations 362

Kleibergen–Paap rk Wald F statistic 206.226

Notes: IV-GMM estimation. Standard errors in parenthesis.�Significant at 90 per cent; Significant at 95 per cent; ��Significant at 99 per cent.


Kleibergen–Paap test statistic for significance, which is well above the critical value. This

means that our estimates are highly unlikely to suffer from inconsistency and bias of less

than 1 per cent which can be a problem when instruments are ‘weak’ (Murray, 2006).

A further issue is that we may be concerned that our sample is different from the total

sample, given that we are only using information on children of a certain age. We have

height and weight information on 254 children in round one who are either not present

in later rounds, or who have missing height or weight data for one of the later rounds. The

435 children, who are in our sample, may be from households that are somehow ‘different’

from the rest of the sample, or be somehow individually different. We may also be

concerned with attrition—our sample includes only those children for whom we have a full

set of anthropometric data across the years to undertake the analysis. Whilst we are not

choosing children other than on their age and the fact that they have data available for all

rounds we are interested in, there may be some underlying differences to those we do not

observe in all rounds (e.g. if weaker, shorter children have died between rounds).

To resolve both these concerns, in Table 7 we compare our sample with the sample of

235 children who were similarly aged in 1994 but who were not included in the analysis

due to missing data. The sample children are slightly shorter but slightly heavier, although

the differences are not significant.

8 CONCLUSIONS

This paper has examined the nutritional status of children when they were aged below

5 years in 1995, and again almost 10 years later in 2004. We find that children who are


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Table 7. Differences between sample and other children

In sample Excluded

Height-for-age �2.56 �2.30

Weight-for-age �1.29 �1.45

Number 435 254

Notes: No significant differences between groups.

1144 C. Porter

stunted when they are under five are twice as likely to be stunted in their early adolescence.

Poorer children are more likely to be stunted (40 per cent are), but even 30 per cent of

children in households that consume above (the very austere) poverty line are stunted;

using a dollar-a-day poverty line, most of the households in this sample would be classified

as poor. There is only very limited evidence of catch-up in nutritional status over the decade

that we have examined. This article adds to the growing evidence that malnutrition at early

ages can have significant impacts even a decade later, and improving nutritional intakes of

children is a serious priority.

On a positive note, despite using a conservative estimation strategy, we do find that food

aid has a significant and lasting impact on child growth, in addition to the safety net that it

provided as relief. There were substantial problems in terms of targeting and delivery of

food aid at the time that the children were young. More effective delivery of social

protection could improve such achievements significantly. Recently, there have been a

number of positive developments in the delivery of the safety net scheme in Ethiopia,

improved targeting, a mix of cash and food and more reliable delivery mechanisms for

payment. Hoddinott et al., (2009) have found that cash and food received under the PSNP

have improved children’s nutrition and schooling in the short-term (though have possibly

increased their working hours, Woldehanna 2009), and this article shows that there is a

good chance that such impacts will last into their future, which is a promising line for future

research when data become available.

ACKNOWLEDGEMENTS

The data used in this paper were collected by the University of Addis Ababa, the

International Food Policy Research Institute (IFPRI), and the Centre for the Study of

African Economies (CSAE). Funding for the survey was provided by the Economic and

Social Research Council (ESRC), the Swedish International Development Agency (SIDA)

and the United States Agency for International Development (USAID).

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Safety nets or investment in the future: Does food aid have any long-term impact on children's growth?