the long run impact of severe shocks in childhood...
TRANSCRIPT
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The long run impact of severe shocks in childhood:
Evidence from the Ethiopian famine of 1984
Catherine Porter*
Centre for the Study of African Economies
Department of Economics
University of Oxford
Preliminary draft- please do not cite
this version
July 2007
Abstract
In 1984, the world was shocked at the scale of a famine in Ethiopia that caused up to a
million deaths. The crisis was brought on by drought from repeated rainfall failure, poor
economic policies and civil war. But what of the survivors? This paper estimates the long-
term impact of the famine twenty years later, on young men and women who experienced
this severe shock as small children during the crisis. The data show considerable
heterogeneity in experiences in famine intensity- around three quarters of our sample had to
cut back on quantities of food eaten, and of those, a third ate only one meal a day. We
model adult health outcomes as a function of childhood inputs using a 2-period model of
childhood development grounded in the human capital and nutrition literature, and identify
the impact of the shock during “critical” or sensitive periods in childhood. We then
examine height-for-age z scores at an intermediate stage ten years after the famine as an
indicator for nutrition progress. At that point, children exposed to the famine aged 1-3 are
shorter in comparison to those exposed as older children, and those born after the famine.
The results indicate the possibility of limited catchup. We discuss the robustness of the
results, including endogeneity of the shock, and positive and negative sample selection
from survival and migration respectively.
* Corresponding address: St Antony’s College, Oxford, OX2 6JF
Email: [email protected]
I would like to thank Stefan Dercon for access to and guidance with the use and analysis of this dataset, also
Sonia Bhalotra and seminar participants at the CSAE, Oxford for helpful comments. The data used in this
paper was 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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1. Introduction and background to the famine
In October 1984 Ethiopia came to the developed world’s attention in a dramatic BBC news
broadcast from Tigray province in the Northern Highlands. The report showed pictures of
starving people on a massive scale and galvanised citizens in Europe and the US into
donating millions of pounds to relief agencies, and putting unprecedented pressure on their
governments to send humanitarian relief. This paper examines what has happened to a
sample of people who experienced this extreme shock as young children by following up
on their height, marriage and educational attainment ten and twenty years on from the
crisis.
Ethiopia has a long and troubled history of famines (Pankhurst (1986a)) including
prolonged droughts and frequent sporadic severe rainfall failure. Since 1984 it has appeared
more than once in the news as being again on the verge of famine. The country has seen
economic growth in the past decade, but seasonal hunger continues today to be an endemic
feature of life in many rural areas, and in 2007 the Government of Ethiopia issued its
annual humanitarian appeal concluding that despite a relatively benign forecast 1.2 million
people will require emergency assistance, and 7.3 million chronically food insecure people
should be covered by the Productive Safety Nets programme1.
However, even with this difficult backdrop, the 1984 famine is still classed as one of the
worst famines ever to have hit Ethiopia, and ranks amongst the worst in recent world
history. The impact of the famine in 1984 was deep and broad, though as is often the case
in a complex emergency, statistics are sparse and unreliable- there is no consensus on the
number of deaths that it caused (see the debate in Pankhurst (1986b), Holcomb and Clay
(1987)), though estimates range from half a million to over a million (the upper bound
being the most popular media quotation). The main regions affected were Tigray, Eritrea
and Wollo in the North of the country, though by 1984 the famine had spread across most
of the country (see map in Annex 1). Warfare played a key role in causing famine in Tigray
even before the drought occurred. Military offensives, aerial bombardment of markets
(destruction of cattle, grain stores, burning of crops), and tight controls on movements of
migrants and traders combined to prevent the normal redistribution of surpluses in Northern
1 Famine Early Warning System Report, accessed 28th Feb 2007
http://www.fews.net/centers/innersections.aspx?pageID=alertDoc&g=1001240&f=et
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Ethiopia (Africa Watch Committee. (1991)). Kiros and Hogan (2001) examine excess
mortality in Tigray during the years 1973 to 1991. They find the expected evidence of high
mortality rates in the areas most affected by war and fighting- across the board, though
parental education reduced child mortality in areas that were marginally affected by
conflict.
Unfortunately there are no official rainfall data available for the districts covered by the
survey during the crisis period. Segele and Lamb (2005) have documented rainfall at a
regional level over a period of 38 years (1961-99) and find that 1984 is extremely
distinctive as the driest overall year- the Kiremt rains started relatively early but then dried
up quickly, leading to an impossibly short effective growing season. They cite rainfall
deficits of up to 94% in Wollo and the Rift Valley. Historical reports by the Ethiopia Relief
and Rehabilitation Commission (e.g. RRC (1984), Human Rights Watch/Africa Watch
Committee. (1991) and other accounts (Gill (1986), Webb et al. (1992), Jansson et al.
(1990)), can further contextualise the development of the crisis. From these sources, we can
see that 1982 was considered a “normal year” of production, though no surveys are
available to corroborate this. In April 1983, however the RRC report was alarming, and the
Meher (main crop2) season of 1983 showed evidence of widespread crop failure.1984 was
by all accounts a year of severe drought- almost in all regions the rains failed in the Belg
(minor crop) season. The drought (from the rainfall data sparsely provided in RRC reports)
can be said to have lasted through 1983 and 1984, and officially ended with the Kiremt
rains that came in 1986.
Relief in the form of food aid was delayed due to political factors- a Marxist regime that
was hostile to US and EU interests, and the Ethiopia Relief and Rehabilitation Commission
was considered by them to be a relief agency with a history of overstating the crisis
statistics. Gill (op. cit., Ch3) notes that many donors were sceptical of the information
provided by the RRC, on both the needs of the population and the amount of available
grain reserves, at a donors’ conference in March 1984 (when excess deaths were already
apparent). Tables 1d and 1e show that grain prices in Northern Ethiopia rose sharply
2 The Meher is the main crop of the year, harvested after the main Kiremt rains. The Kiremt rains account for
65-95 per cent of total annual rainfall in Ethiopia. In some regions (especially south of the Rift Valley) there
are two sets of rain. The minor rainy season is the Belg rain.
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between 1981 and 1985, and also that whilst 1982 appears to have been a bumper crop
year, this fell back substantially in 1984.
Clearly, suffering during the famine was severe and widespread. The aim of this paper is to
examine the longer-term impact of the famine on a sample of young people who were at a
vulnerable age during the crisis, in terms of their future human capital outcomes, and
potentially their adult earnings.
2. Literature on early childhood nutrition and the long-term impacts of famine
Our paper contributes to the literature on nutrition and childhood development as well as
the empirical literature on the short and long term impact of shocks on subsequent human
development. This section reviews them, and highlights a model of human capital
formation that informs the empirical analysis of the paper..
Nutrition in childhood plays a key role in long-term adult physical development. There is
considerable evidence that this development also impacts on productivity (Dasgupta
(1993), Strauss and Thomas (1998), Glewwe et al. (2001)), especially for (the many)
people who are employed in manual labour such as agriculture, wood carrying etc. There
are a number of economic studies relating health and economic outcomes in adulthood, as
well as of early childhood health and subsequent development. The literature in medicine
and epidemiology contains a large number of articles on the influence of childhood
characteristics on adult anthropometric outcomes (Karlberg and Luo (2000), Rona (1981),
Ruel et al. (1995) and for a commentary, Gunnell (2002)). The “foetal origins hypothesis”
incorporates a strong body of epidemiological and biological research that adult outcomes
(especially in terms of health) are strongly influenced by experiences in the womb (for an
overview by a leading expert see Barker (1992)).
Outcomes for health, nutrition and economic success can be mutually reinforcing (and thus,
endogenous) and a key preoccupation of the economic literature has been to try and extract
the causality. Achievement levels are in part genetically determined, but can also be
influenced by environmental factors (including in utero experiences). Almond (2006)
provides an example of the long term health and human capital effects of the 1918
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influenza pandemic on US data of those “in utero” during the crisis. Another example is
found in Case et al. (2005) who look at the effect of childhood economic circumstances on
adult height outcomes, and find substantial impact. In a study of female identical twins
Behrman and Rosenzweig (2004) find a significant correlation between birth weight, adult
physical characteristics and wages. In studies of developed economies, the area of concern
is often links between overconsumption and adult outcomes such as obesity, heart disease,
or diabetes. There are also a number of studies on skill formation. The effects of childhood
poverty on nutritional status in the US have also been examined by Brooks-Gunn and
Duncan (1997), controlling for other family characteristics.
Economic historians have also drawn on this type of anthropometric data to debate
development of well-being over time, and across countries, most controversially by Fogel
and Engerman (1974) in analysing the welfare effects of the history of slavery. Barber and
Dzeniskevich (2005) review the situation of inhabitants of Leningrad during the time it was
besieged during the second world war, when food supplies were cut off, and also find that
there were considerable long-term sociological, psychological and economic repercussions
on the affected population.
Cunha et al. (2005) provide an excellent review article on the economics of human capital
formation. The authors summarise recent empirical findings (mainly on US data) on child
development and provide a theoretical framework that makes intuitive sense, backed up
with a broad range of empirical evidence. The authors postulate a multi-period model of
child investment for a skills production function which can include “sensitive” or even
“critical” periods for producing abilities or skills. In addition, skills produced at an early
stage augment the skills attained at later stages, and indeed improve their productivity
(complementarity, or a multiplier effect). If we assume that childhood has two stages
before adulthood, and denote investment in the child during period t as tI and the skill
produced from the investment as tS for 2,1,1t . Skills could contain a variety of abilities,
but for the first stage of our analysis we will simplify it to height as a proxy for nutritional
achievement which can be considered as ability to farm, carry wood and other manual
tasks, and also as a potential healthy marriage partner. 0S would be the vector of initial
skills at birth (though we can allow for the impact of the famine to affect the foetus in
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utero), and crucially, define the technology of skill formation (or growth) in a recursive
fashion:
),( 11ttt SIfS (1)
Where (.)tf is increasing in ),( 11tt SI and is concave in tI . The technology of skill
formation in (1), determines complementarity or substitutability of investments in different
time periods over time. Thus the assumption that skills in the second period augment
productivity 01
2
1
2 02
Sf
SS , and that higher levels of stocks in skills for the first period
increase the productivity of investments in period 2: 0),(
12
1222
012
2
SISIf and in the extreme
case of perfect complementarity, investments in period two cannot compensate for the lack
of investment in period one3. In sum, the authors assert that early child investments must be
distinguished from late child investments, and, that an equity-efficiency trade-off exists for
late investments, but not for early investment. The extreme case is a kind of “Leontief
technology” in terms of investments over time- non-development at one stage of the life
cycle could lead to a “bottleneck” where it is difficult to develop further. The evidence on
the timing of income (again, mainly on US data) is mixed and the authors call for further
evidence to supplement this knowledge.
This model can show how a severe shock in a critical period of development may lead to
persistent lower levels of achievement in human capital, and is the spirit in which our later
empirical analysis is presented.
There is a broad body of evidence on the role of risk and shocks on welfare outcomes (for a
review see Dercon (2004)). There is considerable evidence that idiosyncratic shocks can be
smoothed, but village-wide (or covariate) shocks cannot and thus household consumption
suffers in the short-term. Dercon and Krishnan examine the short-run impact of shocks on
BMI of adults (Dercon and Krishnan (2000); see also Dercon and Hoddinott (2003) on the
medium-term impact of shocks on consumption). In another study of the short term impacts
of shocks in Ethiopia, Takashi et al. (2005) find that while harvest failure led to child
growth faltering, food aid affected child growth positively and offset the negative effects of
shocks in communities that received food aid. However, many communities that 3 E.g. ),(min 2122 ISfS f and thus skill level attained in period 2 is restricted by period one deficiency.
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experienced shocks did not receive food aid. In sum, the authors find that whilst food aid
has helped reduce child malnutrition, the prevalence of child stunting in Ethiopia remains at
high levels. There is also a growing literature on the benefits of nutritional and other safety
net interventions on child development (Duflo (2000)), again mainly for the short run.
The escalation of suffering from drought to famines is due to a complex mix of causes,
including politics, environment, and conflict and also the creeping nature of suffering
(Dreze and Sen (1990), Devereux (1993)), and this is almost certainly the case for Ethiopia
in 1984. Drought may be the “last straw” in a situation of chronic hunger and poverty,
which then leads to significant numbers of excess deaths. Also, a natural disaster may not
lead to famine if mechanisms are in place to counteract the shock (Sen- also ref in natural
disasters on Ethiopia 2002). Some studies have examined the excess mortality from
famines and economic shocks, which can be indeed said to be the irreversible effects
(Bhalotra (2006)). A number of studies have investigated the impact of economic
conditions on childhood mortality in developed countries, for example Van Den Berg et al.
(2006) find that for Dutch data, the macroeconomic conditions experienced early in life
have a significant (negative) impact on mortality later in life, or reduced life expectancy.
For a review of the broad links between childhood health and economic conditions in
developing countries see Pritchett and Summers (1996).
Relatively few economic studies have attempted to identify the long-term effect of an
extreme event experienced in childhood- though this can be a fruitful line of research both
in terms of building the evidence base for the economic benefits of preventing such crises,
and also if the shocks are exogenous, as further evidence on the role of the childhood
environment in producing adult height outcomes. This section summarises the evidence
from a diverse set of samples. Almond (op cit) estimates the impact of the 1918 influenza
pandemic for those in utero during the crisis. Almond finds a substantial effect and
considers this as evidence in support of the “fetal origins” hypothesis. In another paper,
Maccini and Yang (2006) show early life rainfall to have a significant effect on future adult
outcomes. On a positive side, Maluccio et al. (2006) show lasting improvements from an
experimental nutrition intervention in Guatemala. Meng and Qian (2006) in a study of the
Chinese famine of 1966 raise the possibility of joint determination of outcomes at the
village level due to institutions: Villages with poor storage facilities and poor schools may
have experienced more severe famine and reduced educational attainment for survivors.
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Alternatively villages that experienced severe suffering during the famine may have been
targeted over the past 20 years by government, donor or NGO investments that will have
confounded the effects of the famine with the effects of the programmes.
The econometric section will outline how we can model the relationship between early
nutritional conditions and later health and socioeconomic outcomes, to determine the long-
run impact of the famine on survivors. This is attempted in a paper by Alderman et al.
(2006). The authors study two cohorts of children who were alive during a civil war and
drought in Zimbabwe. They use maternal fixed effects and examine nutritional and
schooling outcomes for children who experienced shocks at a vulnerable age (12-24
months) and find that these are significantly smaller and complete less schooling- using
typical rates of return to education they translate this to a loss in lifetime earnings of seven
per cent. Their work builds on work by Hoddinott and Kinsey (2001).
This paper contributes to the understanding of human capital development by testing
whether an extreme shock experienced at a critical period of early life leads to long-term
deficiencies, and uses a household-level (rather than cohort level) measure of the shock.
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3. Data
The data are from the first and latest (sixth) rounds of the Ethiopian Rural Household
Survey (ERHS) collected by the University of Addis Ababa and the Centre for the Study of
African Economies (CSAE) at the University of Oxford, as well as the International Food
Policy Research Institution (IFPRI), covering fifteen districts4 from five regions. Seven
villages were originally included in IFPRI’s survey of 1989, which were chosen primarily
because they had suffered hardships in the period relevant to this study (e.g. the 1984-85
famine. For a detailed description see Webb et al. (1992). In 1994, 360 of the households
in six villages were retraced and the sample frame was expanded to 1477 households. The
nine additional communities were selected to account for the diversity in the farming
systems in the country5. Within each village, random sampling was used. The households
were resurveyed again in 1994 and 1995, and subsequently in 1997 and 1999. The sixth
and latest round of the survey was completed in 2004. Table 1a (Annex) provides the dates
of surveys in each village. The attrition rate for households is low, around three per cent.
We now turn to a discussion of the data, starting with the sample of children used in the
empirical analysis.
As discussed above, we are interested in the impact of a severe shock on children who are
at a vulnerable age. The shock in question occurred in 1984, and we wish to identify the
impact on children who were aged 12-36 months at that time (cited as the most vulnerable
age for development in the nutrition literature). We therefore take the sample of children
born in 1981-83, and children who are 3-4 years older and younger than them – which
gives us children aged 7-16 in round one of the dataset, ten years after the famine. Children
aged under nine in round 1 would not have been born yet, children aged 9-10 would be
babies, and children aged 13-16 were alive but purportedly more robust, being beyond the
this critical age for development. Tables 1b and 1c show a brief famine history, and the
ages of the children in rounds one and six of the ERHS survey.
4 These communities are called Woredas- the equivalent of a county in the UK. They are further divided into
Peasant Associations (PAs), and consist of up to several villages. The administrative system of the PAs was created in 1974 after the revolution. 5 Although representative, clearly 15 villages is not enough to make strong inference about Ethiopia as a
whole. Also, the choice of the original villages was also questioned in terms of the severity of drought in
those places. This is in fact borne out in the descriptive statistics. We find no evidence that the original
villages suffered more than the nine “new” villages. For a critique/alternative view, see Pankhurst () or
Devereux (2003).
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We are concerned about the possible positive sample selection into this group (i.e. those
who are observed in 1994) due to the excess mortality caused by the famine. It is plausible
to assume that the stronger children will have survived to 1994. We examine the cohort size
and mother’s fertility history to try and piece together the situation. Table 2 shows the
cohort sizes of the children in the full sample. There appears to be a smaller cohort of
children aged 11, 13 and 14. There are 256 children reported age 15. However it is possible
that people have reported a child as age 10 or 12, due to vague recall of the child’s age
(which is a common problem in developing country surveys). However even if we split the
cohorts into 4-year cohorts, the 11-14 year olds as a group are 15% fewer than the younger
children. This is quite a large difference, and may be due to either higher mortality or lower
fertility during the famine. Recall that estimates for mortality were between 0.5 and 1
million out of a population of 70m, mainly in famine areas of Wollo and Tigray (which
includes two of our villages).
A module in the third round of the survey (1995) asks about the fertility history and child
deaths experienced by the wife of head of household (or the head, if she is female). There is
recall data on the birth date and age of children who died, however there are a large number
of missing observations for age and birth date. No questions have been asked specifically
about mortality during the famine and it has not been possible to construct such a figure
from the available data. Further work will attempt to indirectly estimate a cohort-level
mortality effect using more data from Ethiopia, for example the Demographic and Health
Survey (DHS).
Unfortunately we do not have full information on all children ten years later in 2004,
though this is not surprising- given that they are now aged 15-25 and potentially are leaving
home, to set up new families or to migrate for work - indeed the attrition is higher for older
children, as would be expected. Table 3 shows the destiny of children observed in round
one. We know that twenty-four percent of the children who do not appear in round six have
left or moved away- which is not surprising given that they are older than the age at which
many people leave home. There are also three percent who died. There are two problems
with the remaining missing data that we consider to be random. Firstly, there are missing
data (eight percent) for the height of those who are present and we have other information
for them in round six- and around twenty-one percent of people are either missing or the ID
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is not yet matched due to missing roster cards in round six [need to break that down by
age].
To measure the stock of nutritional achievement of the children we use the height-for-age
z-score6 (HAZ). Height-for-age z-scores are recommended by the World Health
Organisation (WHO) as a measure of child development, in particular as a correlate of the
long-run investments in child nutrition (i.e. the “stock” of health). They show the height of
the child relative to a reference group of healthy children, compiled by the National Centre
for Health Statistics (NCHS)7. Controlling for age in our analysis means the results are not
affected by the choice of reference group, but allows us to compare our results with other
empirical studies. A HAZ of minus one for example means that the child’s height is one
standard deviation below the median child of the control group. A HAZ of below minus
two is classified as stunted. Table 6a shows that the sample has poor height for age relative
to the well nourished comparison group. Around forty per cent of children in this group are
stunted (height for age z-score of under minus two). Also, girls are less stunted than boys-
significantly so (at 5%). Christiaensen and Alderman (2004) compare Ethiopian national
data on nutrition over ten years, and show that in 2000, 58% of rural children were stunted,
and 64% in 1992. The sample villages appear then to be slightly better nourished than the
national average. The national data also shows that boys are more stunted than girls,
compared to an international reference. Svedberg (1990) presents evidence that this is the
case in many sub-Saharan African countries, though Klasen (1996) in fact finds evidence of
a slight bias against girls that is rising.
6 Raw data at http://www.cdc.gov/nchs/about/major/nhanes/growthcharts/zscore/zscore.htm
http://www.cdc.gov/nchs/about/major/nhanes/growthcharts/datafiles.htm
7 The NCHS is the standard “healthy population” comparison group for studies of child anthropometrics. The
NCHS is part of the Center for Disease Control and Prevention, a public health body in the US. The WHO
has recently (de Onis M, G. C., Onyango AW, Borghi E., 2007, Comparison of the WHO Child Growth
Standards and the CDC 2000 Growth Charts. Journal of Nutrition 137, 144-148.) compiled a new dataset for
under-5’s based on a broader reference group of healthy children from seven countries (the WHO Multicentre
Growth Reference Study, MGRS). Substantial differences were found compared to the NCHS data,
particularly in very young children (this is mainly due to breastfeeding/weaning practices common in the earlier set). The new reference group is selected from healthy, breastfed children in all of the countries, and is
meant to indicate a more prescriptive approach (e.g. how healthy children should grow- the unconstrained
pattern). In the same symposium in 2006 recommendations were made to introduce new references for older
children, particularly with regard to the shortcomings regarding weight/bmi indicators (body mass
index=weight in kilograms squared, divided by height in centimetres). Until such time, the WHO
recommends the use of the NCHS reference for children over 5.
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We now discuss the famine experiences of the villages, and households, and then return to
the children to analyse the characteristics of children in relation to the famine experience of
the household. In total 962 households (70% of all households) cite drought as one of the
shocks that has affected them in the past 20 years, in a questionnaire that included a variety
of potential crises such as too much rain, pest and diseases, harvest losses in storage, frost
and hailstorms. Respondents were asked to cite the three worst crises of the last twenty
years. Of those who cite drought, the majority (72%) mentioned the years 1975-77 EC
(1983-85 Georgian calendar).
As Table 4 illustrates, there is considerable variation between villages in the famine
experience. As discussed in section one, the famine was most serious in the northern
provinces of Tigray and Wollo, in which three of the villages are situated (Geblen,
Harresaw and Shumsheha). Harerghe (where Adele Keke is situated) in the east was also
badly affected (as well as Afar and Somililand, which are not covered in the ERHS). The
famine did spread across most of the country by 1984 when almost all provinces
experienced a disastrous rainfall failure. In approximately two thirds (10) of the villages,
over 80% of people mention drought as a problem- and in seven of those, more than 70%
cite 1983-85 as the worst year. Imdibir, Doma, Korodegaga, Dinki, Geblen and Haresaw
all report 90% of people who cite difficulties due the famine (rising to100% of people in
Aze Deboa, in Shoa). However, in Adado, which is also in the south of the country
(Sidamo), nobody at all mentions drought as a serious event; also in Sirbana Godeti and the
villages around Debre Berhan few people mention drought. In villages such as Doma, the
proportion of people citing 1984-85 as worst year is quite low, just over half (note that
Doma is a resettlement village8. However, closer inspection of the data reveals that 1983-
85 is the clear mode for all of the villages in which over half of respondents named a
famine year- in all other years there are no more than a handful of observations. This may
be reason for us to suspect noise in the recall period- which is not inconsistent with the
general problem in Ethiopia of trying to get exact dates or ages.
Table 5 shows information on the severity of the drought. 73% of households cut back on
quantities served at mealtimes, and quarter of households ate just one meal per day. 37%
8 About resettlement villages: The Ethiopian government had a policy of resettling. Doma were resettled from
highlands in X year- check what year was the worst in DOMA.
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ate wild foods they would not normally eat and 25% sold assets due to distress. [Food aid
section here].
Tables 6a-d compare the HAZ scores for children of different ages who were exposed to
the famine (in terms of the household reporting 1983-5 as the “worst drought year the
household has faced) compared to those from households who did not report suffering. The
table shows varying results for children exposed at different ages, and for girls and boys.
Looking at the whole sample of children in round one, ten years after the famine, table 6c
shows some significant effects on children both by gender and by age. Relative to the
international norm, boys are shorter than girls at all ages. Interestingly, the famine appears
to have had a greater impact on boys as well- the difference in the average height of boys
from households who had a crisis during 1984 is significant at 1% (and in centimetres is
around 3.5). Disaggregating the gendered effects by age, we see that older boys are not
significantly affected, nor those who were born in the middle of the famine. However boys
who were at a critical age for child development (i.e. aged 12-36 months in the famine) are
significantly shorter if the household was exposed to the famine. The same is true for those
born just after the famine- with perhaps the impact in this case coming through the mother
(ref for that). Surprisingly, the girls, whilst also being taller than the boys, do not show any
significant impact of the shock for any age group.
The smaller sample size including only those for whom we have information in 2004
makes the standard errors larger and the only significant (at 10%) effect is on boys who
were at the vulnerable age of 18-36 months during the famine. The average height at
adulthood/adolescence of those who are observed in 2004 is smaller for both boys and
girls, but significantly so for girls, which could be an indication that weaker girls are less
sought after as potential wives.
Finally, table 7 shows means and standard deviations of the included variables. We use the
HAZ and natural log of height as the dependent variable for the sample of children aged 7-
16 in 1994, and the adolescents/adults in 2004. Note that for adolescents, we can only use
HAZ up to age 24. To proxy the famine shock we include the dummy variable which
indicates the “worst year” for the household, and interact that with the child’s age at the
time of the drought to give an individual-specific measure of famine exposure. Whilst we
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have a rich source of other variables to measure the severity of the drought (e.g. number of
meals eaten, cut back on food consumption etc), these are also the coping mechanisms
which households invoked to mitigate the impact of the famine, and therefore they could be
endogenous. We include controls for birth order, and to minimise the noise in infant
mortality data, we use a dummy on whether the mother has lost one or more children.
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4. Econometric Strategy
We want to test the long term impact of exposure to the famine at an early age on
subsequent child development. In particular, we are interested in whether the famine
affected children who were at a vulnerable age- in utero or just born or those aged 12
months to 36 months- at the time of the famine. The literature cites these two periods as the
most critical time in a child’s life for nutrition- especially if there are cumulative returns to
investment in nutrition and subsequent human development.
Following the concepts outlined in section two above (in particular the model by Cuhna et.
al, and the empirical strategy of Hoddinott et al), we propose a two-stage model of child
development, where health in period one 1iH is determined by a vector of observable
individual and household characteristics 1iZ :
(0.1) 1 1 1 1i i i iH Z1 1 1 1i i i i1 1 1 1H Z1 1 1 1H Z1 1 1 11 1 1 1i i i i1 1 1 1H Z1 1 1 1i i i i1 1 1 11 1 1 1i i i i1 1 1 1i i i i1 1 1 1H Z1 1 1 1H Z1 1 1 11 1 1 1i i i i1 1 1 1H Z1 1 1 1i i i i1 1 1 1
The error term will consist of some unobserved household characteristics (including
parents’ attitude to child nutrition, schooling etc), and some unobserved individual
characteristics (genetic height potential, intelligence etc), and white noise.
(0.2) 1 1i h i1 1i h i1 1i h i1 11 1i h i1 1i h i1 1
Adult outcomes are determined by initial health at period one, and another vector of
observable village household and individual characteristics representing subsequent
investments in health.
(0.3) 2 1 1 2 2 2i i i iA H Z V2 1 1 2 2 2i i i i2 1 1 2 2 2A H Z V2 1 1 2 2 2A H Z V2 1 1 2 2 22 1 1 2 2 2i i i i2 1 1 2 2 2A H Z V2 1 1 2 2 2i i i i2 1 1 2 2 22 1 1 2 2 2i i i i2 1 1 2 2 2i i i i2 1 1 2 2 2A H Z V2 1 1 2 2 2A H Z V2 1 1 2 2 22 1 1 2 2 2i i i i2 1 1 2 2 2A H Z V2 1 1 2 2 2i i i i2 1 1 2 2 2
Where 2iV will consist again of the unobserved household and individual characteristics
(including parents’ attitude to schooling and individual innate ability and motivation)
(0.4) 2 1i h i2 1i h i2 1i h i2 12 1i h i2 1i h i2 1
16
It is clear then that 1 2( ) 0i iE H( ) 01 2( ) 01 2i i1 2i i1 2( ) 0i i( ) 01 2( ) 01 2i i1 2( ) 01 2( ) 0 since initial health will, by definition, be correlated with
the unobservable household and individual characteristics. OLS is biased under these
conditions. Using a sibling fixed-effects model can resolve the correlation between 1iH and
hh (we wish to consider the endogeneity of exposure to the famine - it is the “weaker”
households with fewer coping strategies (and potentially lower ability to invest in children)
who are likely to have been most affected)) but it will still be the case that 1iH is correlated
with ii through the individual unobservables (e.g. genetics). Therefore we will need to use
instrumental variables. Our information on drought shocks can provide this. Although the
shocks may be correlated with hh (this will be removed by the sibling fixed effects), if they
are short-lived, they are not correlated with hh .
Using a sibling fixed-effects model does put considerable demands on the data, and can
also lead to its own sample selection issues (we will be restricting our sample to households
who have at least two children surviving into the age range under scrutiny). In addition, we
will be trying to look at the differential impact between siblings, who may be only a year or
two apart in age- certainly a number of intra household allocation issues are pertinent here.
An alternative strategy is to use a village fixed effects strategy, but to saturate the model
with a high number of controls for household characteristics that may plausibly eliminate
the possibility of the remaining unobservables being correlated with the error. The
variables we use include genetics (parents’ height), household resources (assets,
consumption), income source (farmer, crop type), sibling competition (number of other
children, birth order).
There are a number of econometric concerns that we need to address in order to be
confident that we are correctly estimating the effect of the famine. Firstly, positive
selection bias of the health in the sample of those who were at risk during the famine. The
famine will have had a permanent impact on a large number of children through early
mortality. Stronger, healthier children with better genetic health endowments are more
likely to have survived to 1994 and be included in the first round of the ERHS. In addition,
these children could benefit from reduced cohort sizes. (See data section above for tentative
discussion on a smaller cohort of 10-14 year olds). That could be due to lower fertility
17
during the famine, or high levels of child mortality at that time, (see Kidane (1989;
Lindjorn (1990) for a discussion of mortality in some areas of Ethiopia during the famine).
Both of these effects will tend to bias our estimates of the famine impact downwards, i.e.
make it less likely that we find a significant effect- though we note that in the sibling fixed-
effects specification, any selection bias due to household or village characteristics will be
eliminated. Also, there remains the possibility of selection bias due to non-random attrition
in round six. We find that from observable attrition, around four times as many people have
left due to migration (or leaving the household) than have died, and this potentially could
lead to some upward bias in our estimates. However the majority of the missing data is due
to random (unexplained) factors (such as unmatched roster cards)
Alderman et al find that there are significant impacts for a group of children alive during a
drought in Zimbabwe. Their variable for exposure to shocks consists of number of months
alive prior to civil war ending, and a dummy variable that indicates whether the child was
of a critical age during the famine. Our data improve on this by introducing household
specific measures of the severity of the shock. Individual exposure to the famine is
determined by the interaction of family impact and the age during the worst time- the
assumption being that children in the 12-36 month age group are most at risk and thus
provide heterogeneity in the possible severity of impact. Such a strategy also allays some of
the concerns that may be raised regarding whether a dummy for the age cohort is simply
picking up macroeconomic variables.
5. Results
Firstly we present the results for the height of young adults twenty years on from the
famine which we can think of as being the total stock of health investments in their
lifetime. These are aged 15-26 in round 6 (1994), and include those who were aged 0-7
during the famine, and their younger siblings (who we may think of as an unexposed
group- though notwithstanding the possibility of long term impact on the mother). In total
there are around 800 young adults with non-missing variables for both the sixth and first
rounds. For the younger men and women in the sample, there will be still some potential
for growth, and in addition, growth “spurts” are not uncommon in the late teens/early
twenties which will make the height measurements somewhat noisy.
18
We begin with the specifications using village level fixed effects, shown in table 8a. The
dependent variables are the log of height in round six (column one), and the height-for-age
adolescent z-score for those aged 15-24 (column two). Because of concern about the
potential correlation between the error term and the famine variable, we have used village
level fixed effects (and robust standard errors to control for correlations in the error term at
village level) and household level controls- including ethnicity, height of parents, main
activity (crop) of household, household composition, birth order, child mortality history
from the mother’s records. The control variables show results that concur with empirical
results from the literature on correlates of adult height outcomes. In addition we have used
the “cleanest” drought shock variable- a dummy which equals one if the household
identifies 1983-5 as the year of “most severe drought shock”. It could be argued that some
of the subjective “famine-suffering” variables outlined in the data section are correlated
with the household characteristics that also affect height development- as they represent
suffering but also coping strategies of the household. The specification also includes a full
set of age dummies (reported in the table as the age at the time of the famine (the sixth
round of the survey is timed almost exactly twenty years after the famine, so it is easy to
infer actual age from this). Table 8 (Columns one and two) shows some support for the
hypothesis that the famine impacted younger children- a negative coefficient (significant at
5%) on the drought variable for children who were at the critical age of 12-36 months
during the famine (just under four centimetres). It appears that the height of the unaffected
children in that age group is higher than the average- which was borne out in the
descriptive statistics earlier- pointing to a cohort effect of survivors. None of the other
drought coefficients are significant.
We attempt a sibling-fixed-effects estimate in columns (three) and (four), and none of the
coefficients on the drought shock are significant, though the sample size becomes very
small at this point (467). We need to test whether the village level fixed-effect specification
with household controls such as parent’s height has fully captured the household fixed
effect by measuring correlation in errors across households and then we can be more
confident that this specification plausibly captures the unobserved household effect.
Turning to the first round information- that is ten years after the famine- we examine log
height of the same people at age 5-15 in table 9. We still find significant (more precise, at
19
1%) negative coefficient on the drought shock for those who were aged 1-3 years during
the famine. This is consistent with the results in Hoddinott et al. The height difference from
the mean is 4.6 centimetres, slightly more than the difference ten years later. There is some
limited catch-up between in the adolescent years (Could test this a bit between years- split
into older and younger children). We are unable to estimate the selection effect for these
children in terms of their survival to 1994, but we can interpret the coefficients as the lower
bound on the estimate- since those who suffered most during the famine are no longer alive
to be measured. We may also consider the height-for-age z-score as a more appropriate
measure of nutritional development for children (see discussion above), and column two
presents the results. These are consistent with those for the actual (log) height. The drought
shock has reduced height development by two thirds of a standard deviation for those aged
12-36 months during the famine. An interesting change in the estimates happens when we
introduce sibling fixed effects (columns three and four). The drought coefficient for those
whose families suffered in the year of their birth appears to be smaller. The cohort as a
whole is also taller relative to others. This could be the selection effect outweighing the
shock. Also, it could be argued that in the famine conditions, one of the safest places for a
baby is in the womb or being breast-fed. Hence those children who were recently weaned
have suffered more than those who were still receiving nutrients direct from the mother.
A tentative conclusion may then be that a significant shock to the children in the sample at
a vulnerable age still has discernable impacts ten years later in terms of height achievement.
Twenty years later, we find some evidence that the impact still endures. Further work will
examine the impacts of the drought shock on schooling achievement, marriage, and long-
term illness, though descriptive statistics suggest that the results may be similar to those of
height.
However, there remains the problem of matching the children in round six with those from
round one. As discussed in the data section, we know that there are approximately twenty
percent of the children who have left or moved away- which is not surprising given that
they are older than the age at which many people leave home- if these are the stronger
children then this could bias our results upwards. There are also four percent who died,
which would contribute to a downward bias. The missing data due to lost roster cards or for
other reasons can be presumed to be random, and therefore not leading to any bias per se.
20
6. Conclusion
The results presented show that children who experienced but survived a large scale and
severe nutritional shock at a critical period in their development are discernibly smaller
than their peers twenty years later. The effect is slightly stronger ten years previous, which
suggests some limited catch-up. At age 10, we are concerned about positive sample
selection in terms of healthy children being more likely to be observed through survival- a
reminder that for many children the effect was devastating and permanent. This would
indicate that the results can be interpreted as the lower bound on the effect. For the same
young adults who ten years later are almost at their full height, sample selection between
rounds could be both positive and negative as attrition occurs through death (a small
percentage), migration, and missing data- though our preliminary checks suggest that those
who are still in the household aged 15-25 are more likely to have been affected by the
drought in their childhood. We may consider this to be an interesting finding in itself that
somehow the affected group are less likely to move out and start up their own families.
This study can thus add to a body of knowledge on the long-term impact of severe shocks
and underline the importance of swift intervention in complex emergencies, targeting
children who are at a critical stage in their development. Future versions of this paper will
include robustness checks on height, weight and BMI scores, extending the sample to
include all adults, extending the analysis to include other measures of human capital such
as schooling achievement, and attempting to isolate the effect of food aid on mitigating the
effect of the famine.
21
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24
Annexes
1. Timeline of the Ethiopian Famine, and ERHS survey dates
Table 1a: ERHS survey dates
Round UK calendar Ethiopian Calendar
Round 1 Mar-Jul 94 EC1986
Round 2 Oct 94-Jan 95 EC1987
Round 3 Mar-Jun 95 EC1987
Round 4 Jun/Jul 97, Oct/Nov 97 EC1989 , EC1990
Round 5 Jun-Sep 1999 EC1991 (some into 92)
Round 6 Apr-May 2004 EC1996
Table 1b: Calendar of events in the “1984” famine
EC calendar UK calendar Famine related events
1974 Sept 81-Sept82 Bumper rains in many places during this year- but
war in Tigray/Eritrea
1975 Sept 82-Sept 83 Rains failed this year
1976 Sept 83-Sept 84 Drought, crop failure, war especially in the North
1977 Sept 84-Sept 85 Peak of the famine- widespread hunger, death
1978 Sept 85-Sept 86 Meher rains (towards end 1978, or Aug/Sep 86
marked the end of drought
1979 Sept 86-Sept 87 Crops should have been relatively normal (though
with reduced inputs)
Table 1c: Sampling framework for the analysis
Year of birth Age in 1984 Age in
round 1
Age in
round 6
1977 7 16 26
1978 6 15 25
1979 5 14 24
1980 4 13 23
1981 3* 12 22
1982 2* 11 21
1983 1* 10 20
1984 0-1 9 19
1985 n/a 8 18
1986 n/a 7 17
* denotes vulnerable age as defined in the nutrition literature, dates are using Ethiopian
calendar, 1984-5 is the peak of the famine
25
Table 1d: Grain Prices in Northern Ethiopia
Dates East Tigray North Wollo North Gonder
Nov/Dec 1981 100 50 40
Nov/Dec 1982 165 65 55
Nov/Dec 1983 225 90 45
Nov/Dec 1984 300 160 70
Jun/Jul 1985 380 235 165
Notes: Birr per quintal, source Africawatch (1991) p138
Table 1e: Food production in Ethiopia
Year Total Per head
1977 99 95
1978 110 104
1979 122 113
1980 117 106
1981 115 102
1982 127 110
1983 118 99
1984 110 90
Source: Baulch() (NB this does not include Tigray,
and population figures are disputed.)
26
Table 2: Cohort size of children
born during or just before the famine
Age in round
one (birth year
in brackets)
Frequency
3 (1991) 265
4 (1990) 263
5 (1989) 247
6 (1988) 297
7 (1987) 270
8 (1986) 258
9 (1985) 270
10 (1984) 256
11 (1983) 211
12 (1982) 274
13 (1981) 216
14 (1980) 203
Total 3,030
Note: cohort size includes all children in the
ERHS dataset, not only those with non-missing
data for the purposes of the analysis.
Table 3: Round six status of children observed in round one
Status Frequency % of total
observed 811 0.426
missing status 405 0.213
left/moved away 474 0.249
Died 58 0.030
observed, height missing 156 0.082
Total sample of children in 1994 1904
27
Table 4: Village drought experiences
Proportion of Households affected by drought, and worst drought year
Peasant association Drought affected Drought in 83-5 Worst year 83-5
Haresaw 0.935 0.844 0.597
Geblen 0.989 0.981 0.925
Dinki 0.959 0.940 0.902
Yetemen 0.681 0.405 0.333
Shumsha 0.875 0.797 0.793
Sirbana Godeti 0.094 0.036 0.036
Adele Keke 0.677 0.599 0.595
Korodegaga 0.939 0.903 0.797
Trirufe Ketchema 0.600 0.580 0.580
Imdibir 0.889 0.889 0.790
Aze Deboa 1.000 1.000 1.000
Adado 0.000 0.000 0.000
Gara Godo 0.985 0.960 0.925
Doma 0.948 0.712 0.566
Debre Berhan. -Milki 0.344 0.325 0.308
D.B. -Kormargefia 0.273 0.172 0.166
D.B. -Karafino 0.294 0.246 0.246
D.B. -Bokafia 0.193 0.193 0.161
Notes: 1) Specifically, responded “drought” to the question “In the last 20 years has the household suffered a
substantial loss of harvest through any of the following [list of potential crises] ?
2) Households were asked to list the three worst crises, this entry is positive if the household responds EC75,
EC76 or EC77 (1983-85)
3) In the list from (2), household ranks EC75, EC76 or EC77 (1983-5) as the worst crisis.
28
Table 5: Household drought experiences
Suffering during the famine, by village
PA Nr. meals Cut back Ate wild food Sold assets Did all four
Haresaw 1.777 0.930 0.057 0.056 0.000
Geblen 1.428 0.957 0.858 0.258 0.144
Dinki 1.093 1.000 0.782 0.274 0.225
Yetemen 2.132 0.811 0.065 0.093 0.026
Shumsha 1.292 0.974 0.343 0.255 0.134
Sirbana Godeti 2.545 0.190 0.000 0.076 0.000
Adele Keke 1.701 0.880 0.827 0.614 0.156
Korodegaga 1.998 0.945 0.619 0.291 0.035
Trirufe Ketchema 2.284 0.706 0.269 0.173 0.033
Imdibir 2.039 0.827 0.167 0.264 0.023
Aze Deboa 1.575 0.946 0.429 0.386 0.205
Adado 2.000 0.000 0.000 0.000 0.000
Gara Godo 1.576 0.929 0.995 0.787 0.358
Doma 2.387 0.915 0.741 0.482 0.012
Debre Berhan. -Milki 2.807 0.500 0.013 0.065 0.000
D.B. -Kormargefia 2.888 0.471 0.000 0.024 0.000
D.B. -Karafino 2.761 0.700 0.000 0.283 0.000
D.B. -Bokafia 2.955 0.345 0.148 0.000 0.000
Table shows the proportion of households in the village to undertake the coping strategies
of reducing the number of meals, cutting back on quantities, eating wild food they would
not normally eat, selling assets. The final column shows those who did all of them.
29
Table 6a: Height for age Z-scores ten years after the famine
Children aged 5-15 in 1994, who are traced in 2004
Drought
affected Not affected Difference T-statistic
No.
Obs
All -2.144 -1.956 0.188 1.539 811
Boys -2.221 -2.113 0.107 0.685 461
Girls -2.043 -1.745 0.298 1.524 345
Table 6b: Height for age Z-scores: Above sample disaggregated by gender and age
Drought
affected Not affected Difference T-statistic No. Obs
BOYS ONLY
Born just after famine -2.185 -2.174 0.011 0.039 196
During famine -1.777 -2.421 -0.645 1.374 42
Age 1-3 in famine -2.325 -1.821 0.505
1.770+ 128
Age 4-7 in famine -2.348 -2.266 0.082 0.293 95
GIRLS ONLY
Born just after famine -1.845 -1.760 0.085 0.292 149
During famine -2.010 -1.779 0.231 0.436 48
Age 1-3 in famine -2.278 -1.674 0.604 1.555 90
Age 4-7 in famine -2.162 -1.779 0.383 0.757 58
Notes: "Drought affected" specifically refers to the household indicating that 1983-5 were
the worst years when asked about a 20 year history of vulnerability.
"Born just after famine" indicates child was born in 1985-88
"During famine" indicates child was born in 1984
"Age 1-3 in famine" indicates child was born in 1981-83
"Age 4-7 in famine" indicates child was born in 1978-82
“Sample” refers to the subset children who have a non-missing height entry in round 6.
30
Table 6c: Height for age Z-scores ten years after the famine
All children aged 5-15 in 1994 (including those who have migrated, died, or are
otherwise missing)
Drought
affected Not affected Difference
T-
statistic No. Obs
All -1.910 -1.736 0.174 2.263+ 1904
Boys -2.150 -1.849 0.301 2.885** 965
Girls -1.658 -1.623 0.035 0.311 929
Table 6d: Height for age Z-scores of above sample disaggregated by gender and age
Drought
affected Not affected Difference
T-
statistic No. Obs
BOYS ONLY
Born just after famine -2.261 -1.712 0.549
2.532** 318
During famine -1.702 -2.049 -0.346 0.968 85
Age 1-3 in famine -2.044 -1.597 0.446 2.336* 257
Age 4-7 in famine -2.253 -2.107 0.146 0.983 305
GIRLS ONLY
Born just after famine -1.464 -1.491 -0.027 0.124 309
During famine -1.601 -1.438 0.164 0.504 97
Age 1-3 in famine -1.817 -1.738 0.078 0.370 248
Age 4-7 in famine -1.738 -1.751 -0.013 0.072 275
Notes: "Drought affected" specifically refers to the household indicating that 1983-5 were
the worst years when asked about a 20 year history of vulnerability.
"Born just after famine" indicates child was born in 1985-88
"During famine" indicates child was born in 1984
"Age 1-3 in famine" indicates child was born in 1981-83
"Age 4-7 in famine" indicates child was born in 1978-82
31
Table 7: Means and standard deviations of variables
Variable description mean std. deviation
Height (cm) round 1 112.338 29.449
Height (cm) round6 148.233 18.283
Ln Height (cm) round 1 4.683 0.287
Ln Height (cm) round6 4.989 0.152
Height-for-age Z-Score round 1 -1.837 1.659
Sex (female=2) 1.496 0.500
Age round 1 7.827 4.776
Born EC 1970 0.043 0.202
Born EC 1971 0.060 0.238
Born EC 1972 0.051 0.221
Born EC 1973 0.047 0.212
Born EC 1974 0.060 0.238
Born EC 1975 0.054 0.226
Born EC 1976 0.063 0.242
Born EC 1977 0.064 0.245
Born EC 1978 0.065 0.247
Born EC 1979 0.066 0.249
Born EC 1980 0.080 0.271
Older child (interacted with drought) 0.119 0.323
Younger child (interacted with drought) 0.107 0.309
Born in 77 (interacted with drought) 0.036 0.186
Birth order 1 0.173 0.378
birth2 0.198 0.399
birth3 0.194 0.396
birth4 0.145 0.352
birth5 0.116 0.320
birth6 0.073 0.260
birth7 0.042 0.202
birth8 0.023 0.149
birth9 0.036 0.186
Height of household head 166.501 7.931
Weight of household head 55.303 7.756
Household size 7.784 3.161
Real consumption per adult equivalent r1 74.905 69.627
32
Table 8: Regression results, height of young adults 20 years after the
famine
(1) (2) (3) (4)
Dependent variable Log height Height/age z-score Log height Height/age z-score
aged 26 0.0834** (dropped) 0.0478 (dropped)
(3.73) (1.18)
aged 25 0.0857** (dropped) 0.112+ (dropped)
(5.15) (1.82)
aged 24 0.0935** 0.520 0.0826** 2.032*
(5.88) (0.95) (3.63) (2.55)
aged 23 0.0938** 1.101* 0.0791** 0.998+
(5.22) (2.40) (3.39) (1.95)
aged 22 0.0835** 1.219** 0.0700** 0.788*
(4.85) (4.23) (3.54) (1.99)
aged 21 0.101** 1.585** 0.0801** 0.977*
(6.18) (5.61) (3.88) (2.31)
aged 20 0.0925** 1.417** 0.0653* 0.578
(6.34) (5.96) (2.45) (1.52)
aged 19 0.0673** 0.833** 0.0345+ -0.0781
(4.39) (3.06) (1.93) (-0.21)
Aged 18 0.0512** 0.533* 0.0441+ 0.203
(3.38) (2.31) (1.95) (0.56)
Aged 17 0.0356* 0.494* 0.0398+ 0.335
(2.09) (2.23) (1.69) (0.89)
Sex of HH member -0.0326** 0.695** -0.0264* 0.778**
(-4.28) (5.01) (-2.03) (3.13)
Drought*older child -0.00432 0.420 0.00603 0.204
(-0.34) (0.86) (0.23) (0.33)
Drought*young child -0.0261* -0.526* 0.00565 0.137
(-2.40) (-2.34) (0.23) (0.33)
Drought*born in 84 -0.00455 0.0105 0.0212 0.636
(-0.29) (0.03) (0.81) (1.25)
Constant 4.919** -5.766** 5.042** -3.407**
(48.43) (-3.14) (164.55) (-8.17)
Sibling fixed effects? no no yes yes
Household level controls? yes yes - -
Village level fixed effects? yes yes - -
Adjusted R-squared 0.106 0.110 0.157 0.162
Observations 759 663 531 467
t statistics in parentheses
+ p<0.10, * p<0.05, ** p<0.01
Notes: "Drought " specifically refers to the household indicating that 1983-5 were the
worst years when asked about a 20 year history of vulnerability.
"born in 84" indicates child was born in 1984
"young child" indicates child was born in 1981-83, therefore aged 12-36 months in famine
"older child" indicates child was born in 1978-82, therefore aged 4-7 years in famine
Sample is young adults aged 14-26 (14-24 for the height-for-age z-scores, as WHO does not
recommend adolescent HAZ scores above that age) in round 6 of the ERHS survey (2004).
Household controls include height of father, household size, main activity and crop of
household, ethnicity, birth order.
33
Table 9: Regression results, height of children 10 years after the famine
(1) (2) (3) (4)
Dependent variable Log height Height/age z-score Log height Height/age z-score
age 16 0.376** 0.328 0.353** 0.0133
(20.17) (0.85) (11.75) (0.02)
aged 15 0.332** -0.125 0.312** -0.512
(19.69) (-0.37) (15.35) (-1.28)
aged 14 0.322** 0.164 0.308** -0.116
(19.26) (0.49) (16.73) (-0.31)
aged 13 0.302** 0.411 0.289** 0.137
(19.33) (1.28) (16.91) (0.39)
aged 12 0.249** 0.215 0.243** 0.0299
(17.85) (0.79) (19.68) (0.12)
aged 11 0.253** 1.072** 0.205** 0.0553
(16.46) (3.43) (12.17) (0.16)
aged 10 0.190** 0.598* 0.177** 0.313
(13.82) (2.12) (12.77) (1.13)
aged 9 0.134** 0.177 0.121** -0.147
(7.86) (0.50) (7.13) (-0.43)
Age 8 0.106** 0.396 0.111** 0.427+
(8.73) (1.57) (9.11) (1.73)
Age 7 0.0652** 0.450* 0.0534** 0.198
(6.30) (2.06) (4.15) (0.76)
Sex of HH member 0.00471 0.215+ 0.0143* 0.393**
(0.77) (1.75) (2.00) (2.75)
Drought*older child -0.00725 -0.203 0.0142 0.247
(-0.54) (-0.76) (0.78) (0.65)
Drought*young child -0.0348** -0.698** 0.00330 0.116
(-2.78) (-2.82) (0.24) (0.42)
Drought*born in 84 0.00658 0.173 0.0405* 0.977*
(0.34) (0.44) (2.04) (2.45)
Constant 4.715** -1.496 4.630** -2.924**
(70.13) (-1.10) (353.43) (-11.36)
Sibling fixed effects? no no yes yes
Household level controls? yes yes - -
Village level fixed effects? yes yes - -
Adjusted R-squared 0.652 0.026 0.811 0.059
Observations 759 759 531 531
t statistics in parentheses
+ p<0.10, * p<0.05, ** p<0.01
Notes: "Drought " specifically refers to the household indicating that 1983-5 were the
worst years when asked about a 20 year history of vulnerability.
"born in 84" indicates child was born in 1984
"young child" indicates child was born in 1981-83, therefore aged 12-36 months in famine
"older child" indicates child was born in 1978-82, therefore aged 4-7 years in famine
Sample is young adults aged 6-16 in round 1 of the ERHS survey (1994).
Household controls include height of father, household size, main activity and crop of
household, ethnicity, birth order.
34
Figures
1.Non-parametric regressions: height and age ten and twenty years after famine
-3-2
.5-2
-1.5
HA
Z-s
co
re
5.00 10.00 15.00Age
non-affected drought-affected
Height - for - age z - scores : boys aged 5 - 15
-2-1
.8-1
.6-1
.4-1
.2-1
HA
Z-s
co
re
5.00 10.00 15.00Age
non-affected drought-affected
Height - for - age z - scores : girls aged 5 - 15
4.9
5
5
5.0
55.1
5.1
5
ln-h
eig
ht
15 20 25Age
non-affected drought-affected
Log Height : men aged 15 - 25
4.9
8
5
5.0
25.0
45.0
6
ln-h
eig
ht
15 20 25Age
non-affected drought-affected
Log Height : women aged 15 - 25
Notes: graphs created using “lowess” command in STATA, with bandwith 0.8 (default). Broken
lines in every graph are people where the household head has indicated that 1983-5 was the “worst
drought” of the past 20 years. Other variables are defined in the same way as in tables.