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  • 8/11/2019 The Response of Child Nutrition to Changes in Income.pdf

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    The Response of Child Nutrition to Changes in Income:

    Linking Biology with Economics*

    Harold Aldermany

    y World Bank 1818 H St., Washington, DC 20433, USA. e-mail: [email protected]

    Abstract

    It is regularly pointed out that despite impressive economic growth India has the largest

    number of malnourished children in the world. It also has the largest number of people with

    diabetes despite moderate levels of obesity. These two observations may in fact be linked;

    poverty breeds malnutrition in children and chronic diseases in adults. This article reviews

    evidence on the critical role of early nutrition both for long-term health and also for reducing

    the intergenerational transmission of poverty. (JEL codes: I12, I14, O15)

    Keywords: nutrition, child development

    1 Introduction

    There is likely little doubt on the validity of either the proposition that

    income growth reduces malnutrition or that reducing malnutrition

    promotes income growth. How much emphasis is placed on the latter,

    however, depends, in part, on the expectations on the magnitude of theformer. In particular, if there is a sharp gradient of nutritional status as

    one moves away from poverty then a different set of programs might be

    prioritized than ifas evidence in Haddad et al. (2003)impliesincome

    growth even when evenly distributed over a population has a relatively

    modest, albeit significantly positive, impact on undernutrition rates.

    Because it is comparatively easy to derive quasi-reduced form estimates

    of the association of child malnutrition and current household income this

    relationship can be used to both guide development priorities as well as

    address pragmatic operational questions such as how much transfer is

    needed to effect a meaningful change in health-seeking behavior

    (Fernald et al. 2008). This article therefore begins with a discussion of

    two observations on the relation of income and income growth to nutri-

    tional status. One observation is on the magnitude of the association of

    income and nutrition and the other focuses on evidence on the timing of

    income. The next section explores new evidence on how these economic

    paths may relate to aspects of physical growth again choosing to highlight

    two particular features: (i) the timing of critical periods of growth and (ii)

    some recent studies that potentially have a bearing on why obesity and

    * Paper prepared for CESifo workshop on Malnutrition in South Asia Venice InternationalUniversity, San Servolo, Venice 2021 July 2011.

    The Author 2012. Published by Oxford University Presson behalf of Ifo Institute, Munich. All rights reserved.For permissions, please email: [email protected] 256

    CESifo Economic Studies, Vol. 58, 2/2012, 256273 doi:10.1093/cesifo/ifs012Advance Access publication 2 May 2012

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    malnutrition are often observed in the same households, never mind the

    same communities. The final section links these themes.

    2 Some observation on the path from income growth to child

    growth

    A. The global evidence that income growth is not a sufficient condition for

    the rapid reduction of malnutrition

    On average, country-level rates of undernutritiondefined as low weights

    for agedecline at roughly 50% of the rate that gross national products

    (GNP) per capita increase. This cross country evidence differs little from

    results generally derived from household surveys at the individual level(Haddad et al. 2003). Likewise, anemiadefined as hemoglobin levels

    below 10.9 g/dldeclines at roughly 25% of the rate of income growth

    (Alderman and Linnemayr 2009).

    The majority of studies reporting such evidences are based on

    cross-sectional surveys and, as always, there are challenges to the causal

    interpretation of these associations. However, since anthropometric

    surveys are increasingly available, one main issue for deriving such esti-

    mates at the individual level for any population or subpopulation strati-

    fied by classifications such as gender or age is the source of expenditure orincome data. Many data sets with information on anthropometric meas-

    urements or on anemia such as Demographic and Health Surveys [DHS]

    lack income or expenditure information.Filmer and Pritchett (2001)have,

    however, shown that asset indices can be conveniently used to map the

    relationship of economic status and nutrition. This techniquenow

    widely employedalso addresses the fact that a graphic or regressional

    analysis of the association of an outcome with one that is measured with

    substantial error (as are expenditures and income) is biased towards zero

    in proportion to the measurement error. The construction of an assetindex sweeps out most measurement error and thus reduces this bias

    appreciably.

    Alternatively, a measure of income or expenditures that is predicted

    from assets also has this appreciable advantage over directly reported

    income. This treatment for errors in variables is closely related to the

    use of instrumental variables to address the simultaneity or endogenity

    of income, although the issue of exclusion restrictions does not necessarily

    arise when considering errors in variable bias. In any case, the mismeas-

    urement of income or expenditures is plausibly the dominant issue relative

    to the lagged endogeneity of assets. That is, the former is known to be

    appreciable while the latter is unknown. While very young children do not

    directly contribute to asset accumulation, other possible sources of bias

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    may reflect either the impact of a childs illness on parents ability toacquire assets or the correlation of unobserved preferences for savings

    over current consumption and health seeking behavior.1

    An asset index provides only an ordinal proxy for income or wealth.

    Often the index is reported in quintiles as illustrated inTable 1or similar

    aggregations. Such tables confirm that malnutrition rates are high even

    among those who are not poor. For example, using the data inTable 1,

    where all households in the lowest two quintiles by wealth in Pakistan to

    have the characteristics of the middle quintile, poverty in the country

    would be virtually eliminated, yet over 38% of the entire population ofchildren would still be malnourished.

    Such an association of malnutrition and asset rankings can be used to

    derive an approximation of income elasticities if the ranking of assets can be

    associated with the average income of each quintile using alternative data.

    Similarly, it is straightforward to construct average elasticities of income

    Table 1 Nutrition and poverty: prevalence of child underweight by wealth

    quintiles

    Region Country Lowest 2nd 3rd 4th Highest

    South Asia

    Bangladesh 59 53 45 43 30

    India 61 54 49 39 26

    Pakistan 54 47 43 37 26

    Africa

    Benin 29 30 23 20 10

    Burkina Faso 42 40 41 39 22

    Ethiopia 49 51 51 45 37

    Mozambique 31 28 26 19 9

    Rwanda 27 30 28 24 14

    Tanzania 25 26 22 20 12

    Uganda 27 26 25 19 12

    Source: Gwatkin et al., Country Reports on HNP and Poverty: Socio-economic Differences

    in Health, Nutrition and Population, April 2007.

    Note: Data for children

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    growth at an aggregate level. For example, in the 1990s, India experienced a

    growth rate in GDP per capita of 5.3% whereas malnutrition declined by

    1.5% (World Bank 2006). The implied average elasticity of0.28 is lower

    than the rule of thumb of0.5 mentioned above and lower than that ofChina (0.67) or Bangladesh (0.73) over the same period.

    Indeed, results from India on the prospects of addressing malnutrition

    primarily through income growth are generally discouraging. For exam-

    ple, Subramanyam et al. (2011) find that state-level economic growth in

    India offers little or no explanation for reductions in malnutrition between

    1992 and 2006. Since this analysis of three rounds of the National Family

    Health Survey also includes family wealth among the covariates and since

    there were strong gradients of the risk of underweight with poverty as well

    as maternal and paternal education in this data, the result hints at the roleof state infrastructure and execution of its programs rather than private

    resources. This does not completely remove the puzzle of the relatively low

    response to growth, but it does help locate it.

    Still, for many purposes a cardinal estimate of the impact of income

    growth is required at a more disaggregated level. Even in the absence of

    direct survey data on income or expenditures it is nevertheless often pos-

    sible to map expenditures to the same assets that are commonly used to

    construct an asset index using data from an income and expenditure

    survey and then use these parameters to predict expenditures within aDHS or similar survey that has information on assets. Such an approach

    was used within the context of a randomized longitudinal trial of a com-

    munity nutrition program in rural Uganda. This estimate found that while

    income growth will have a positive and statistically significant impact on

    undernutrition, a 5% rate of per capita income growthsubstantially

    larger than the average for the decade that preceded the studywould

    take 33 years to reduce underweight rates by half (Alderman 2007).

    As mentioned, such indications of the magnitude of improvements given

    income growth can be useful in policy projections. Such a starting pointmay, however, not fully guide the design of cash transfers support programs

    since there is some evidence that the source of income matters, either

    through labeling effects (Kooreman 2000; Paxson and Schady 2010) or

    through gender control. Ideally, to understand the path from income to

    nutrition one would like to complement a reduced form approach with a

    nutrition production function to gain insights on the contribution of key

    inputs in overall health as well as assess how income and prices affect the

    demand for these inputs. While health production functions are likely to be

    more stable than reduced form estimates (Rosenzweig and Schultz 1988),

    the difficulty in finding suitable identifying restrictions for input demand

    remains an obstacle to their application. Randomized controlled trials have

    served to derive the impact of selective inputs, particularly micronutrients,

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    but as is increasingly recognized it is still the exception that the heterogen-

    eity of response within a population is addressed in such studies.

    Additionally, the issue of substitution and complementarity of inputs is

    generally not assessed in randomized trials.

    B. Timing of income seems to matter

    The evidence discussed above, being based on instrumented expenditures

    or income and thus indirectly on assets, should reflect a measure of

    permanent income. Why then, is the evidence that short-term shocks to

    income have long-term consequences on nutritional outcomes so perva-

    sive? In one example from this body of evidence,Alderman et al. (2006)

    found that children who were 2 years old during a period of drought and

    civil unrest in Zimbabwe were more malnourished than their siblings.

    Moreover, this shock has a long reach; when this cohort was tracked

    over two decades, the malnourished children were found to be both

    shorter and had fewer grades completed than other children.

    Shocks need not even be of a magnitude commonly deemed droughts to

    have an impact on child nutrition.Maccini and Yang (2009)look at rain-

    fall in Indonesia and find girls born in years of low rainfall achieved

    smaller adult heights and also have less schooling than those born in

    years with normal rainfall. The study notes that the impact of a shortfall

    of rain in a year immediately prior to birth does not have a similar mea-

    sureable impact on nutrition. Recent research using the Indian National

    Family and Health Survey and an international database of natural dis-

    asters is in keeping with this result (Datar et al. 2011). This study finds that

    small to moderate disasters reduce child growth as well as lead to

    increased morbidity, with the impact being greater for girls. The study

    also found that children who were post weaning were most vulnerable.

    The timing of nutritional shocks is not only found when comparing

    across years. Birth outcomes reflect intra-year patterns as well.Lokshin

    and Radyakin (2012) find that Indian children born in the monsoonmonths have lower anthropometric scores than children born in the

    post-monsoon period. This holds for both girls and boys and the results

    vary little between the ordinary least squares estimates and the family

    fixed effects estimates (on a much smaller sample).2 Wealth and education

    mitigate these seasonal patterns without eliminating them, whereas rainfall

    at the time of a childs birth has little impact. Lokshin and Radyakin,

    however, cannot determine at what age this pattern kicks in, although it is

    clearly prior to the age of 3 years.

    2 AsAlderman et al. (2011)point out, selective survival is unlikely to influence anthropo-metric patterns at the level of mortality in South Asia.

    260 CESifo Economic Studies, 58, 2/2012

    H. Alderman

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    Studies of the timings of nutritional insults, however, seldom distinguish

    the impact of prices from that of liquidity. Changes in relative prices,

    whether for food or for child care, can plausibly account for a share of

    either seasonal or inter-year weather-related patterns of malnutrition. Butfood prices cannot be the entire story since similar patterns of increased

    mortality in light of financial shocks have been regularly noted. For exam-

    ple,Baird et al. (2011)confirmed that macroeconomic shocks, defined as

    deviation from trends in 59 developing countries were associated with

    increased infant mortality particularly for girls. However, shocks that

    occurred during gestation did not seem to affect mortality. These results

    are in keeping with those for India based on family fixed effects estima-

    tions (Bhalotra 2010). Again, this study finds that income shocks increase

    mortality, particularly of girls. It also notes that shocks in utero have asmaller (marginally significant) impact relative to those in the neonatal

    period while post-neonatal shocks do not affect mortality. When

    rainfallthe impact of which is presumed to be additional to any

    income effect and thus may be considered a proxy for disease environ-

    mentwas included the aggregate shock decreased but not substantially.

    Health care expenditures andgiven the measured increased labor of

    mothers in times of distresschild care were found to decrease in times

    of aggregate income shocks. Finally, Bhalotra finds that the aggregate

    shocks affect children of uneducated and younger mothers more thanthey affect others.

    In the absence of evidence on massive selection bias, it is reasonable to

    assume that nutrition results move in a similar manner as these income

    and mortality patterns particularly if they are driven by health seeking

    and child care. Note, however, that these results are observed after any

    government, non-governmental organization, or family responses to the

    crises. Even if a public safety net cannot gear up expenditures and cannot

    refine its targeting rapidly enough to address an aggregate shock, it is a

    bit of a puzzle why families are unable to buffer their expenditures usingtheir own and community resources. This is especially true for seasonal

    patterns; Paxson (1993) notes that seasonality in Thailand is mainly an

    issue of prices and not total expenditures. Indeed, while the phenomenon

    of seasonality of consumption is widespread, it is hard to explain in

    terms of intertemporal utility theory. Even income fluctuations between

    years are often protected by household savings and other measures to

    self insure or to co-insure within a community. However, repeated or

    presumablylong-term shocks may have different impact (Alderman

    1996). As there are clear mechanisms to transfer income, but few to

    transfer time available for child care, it remains important to determine

    the manner by which these income shocks transmit to the health of

    children.

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    3 Two features of child growth particularly relevant for

    economic inquiry

    A. Timing matters from a biological standpointThe literature on human development often refers to critical and sensitive

    periods after which it is either impossible or at least difficult (and expen-

    sive) to offset a lost opportunity for physical and cognitive development.

    Given the obvious importance of understanding the timing of interven-

    tions, the temporal sensitivity of undernutrition risks has been the focus of

    a range of studies.Alderman et al. (2006), for example, identify impacts on

    the assumption that children under 2 years are more vulnerable than

    slightly older ones. Likewise, Maccini and Yang (2009) interpret their

    results as implying the weather shocks they study have an effect duringthe critical period of weaning rather than the prenatal period. The limited

    period of vulnerability is consistent with extensive epidemiological

    evidence that shows most growth faltering occurs in the first 18 months

    of life (Victora et al. 2010).3 This reflects both the risk of diarrheal disease,

    especially for children who are not exclusively breastfed until 6 months, as

    well as the overall deceleration of growth in infancy. Based on the lower

    bound of the average change across the 5th, 50th, and 95th percentiles in

    the new growth curves published by World Health Organization, a child

    can be expected to grow 24 cm in year 1 and only 12 cm in year 2 and lessin its third year.

    Similar critical periods of vulnerability to nutritional shocks are

    observed in pregnancy. For example, one of the earliest finding of the

    Dutch Famine study was that women in their third trimester of pregnancy

    during the relatively brief period of severe food shortages in the winter of

    1944/5 were at increased risk of low birth weight babies (Stein et al. 1975).

    This risk was not noted for women who conceived later and, thus, who

    entered the third trimester after the famine was relieved.

    Turning such observations around, clinical and epidemiological studies

    have pinpointed optimal times for intervention. For example, folate

    supplementation is most likely to prevent birth defects if available

    prior to conception and iodine status is also more important in early

    pregnancy than after the child is born. In a study in the Gambia, Ceesay

    et al. (1997) provided supplements to women in the 20 weeks prior to

    expected delivery and found a six percentage points reduction in the

    prevalence of low birth weight. Notably, the mean birth weight among

    the intervention group increased by a larger amount in the nutritionally

    poor hungry season.

    3 This does not rule out catch up growth, for example, during adolescence. With increaseavailability of longitudinal data this is an area of promising research.

    262 CESifo Economic Studies, 58, 2/2012

    H. Alderman

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    The timing of pregnancy itself is important for the outcome of preg-

    nancy in another sense as well. Birth weight is lower and mortality risk

    higher when a pregnancy occurs prior to age 18a time when a woman is

    still growingalthough the relative contributions to birth outcomes ofyoung women of biology, knowledge and practices for health, and

    family wealth are hard to assess due to the endogeneity of reproductive

    choices. But one critical period for pregnancy outcomes occurs long before

    conception; the evidence on the role of maternal nutrition on the next

    generation is quite strong. For example, using 109 DHS data sets from

    54 countries,Ozatlin et al. (2010)found a negative association of risk of

    child mortality and maternal height in 52 countries, with 46 of these

    statistically significant. This was found for all regions including Europe

    (although the risk was somewhat less in Africa) and was robust whenhousehold wealth rankings were taken into consideration. Thus, while

    the association was not driven by differences in current wealth or

    income it does reflect the wealth of the mothers natal family.

    This indicator of the inter-generational transmission of healthand,

    given other evidence, the transmission of povertyis fairly general; mater-

    nal stature is almost always a significant determinant of child anthropom-

    etry, even when other wealth and education variables are included in the

    estimates (Black et al. 2008).4 This pattern, often obtained in cross section,

    but a similar finding has also been shown in results of a randomized trialin Guatemala conducted between 1969 and 1977 (the participants of which

    are still being tracked). This research found that the children of women

    who received nutritional supplements in childhood had higher birth

    weights as well as better anthropometric indicators of nutritional status

    (Behrman et al. 2009). The precise pathway of this generational impact

    could not be elucidated as the benefits of this intervention also included

    higher education of the mothers who were supplemented when they were

    children although a physiological contribution is consistent with the wider

    literature.This particular study did not find any influence of next generations

    nutrition from the participation of the father in the intervention as a

    child. Often, but not universally, paternal stature also has a smaller coef-

    ficient in cross-sectional estimates (Alderman 1990). Assuming that errors

    in variables are not driving this (as it might if paternity was regularly

    misassigned), it implies that phenotype influences the childs health in

    4 The reference cited here is part of a series of articles that review the state of the art fornutrition. The reader who wants an introduction to the consensus views in the field (to thedegree that any discipline remains in consensus) might begin with this series. See also,Victora et al. (2008)andBhutta et al. (2008).

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    addition to genotype. As height represents the choices and conditions that

    prevailed when the mother was herself a child, this is a strong indication of

    the transmission of one generations wealth (among other factors) to the

    health of the next.

    The timing for the impact of an intervention is shown in a few published

    studies. Community nutrition programs that stress changes in care giving

    and health seeking behavior but which do not provide macro-nutrient

    supplementation or income support generally find the initial impacts

    only among the youngest children in the treatment group (Linnemayr

    and Alderman 2011,Alderman 2007). This is in keeping with the import-

    ance of exclusive breast feeding and diarrheal disease prevention in the

    first months of a childs life.

    The provision of supplementary foods for weaning age children havebeen shown to make a difference, but, again, it is important to fine tune

    the age of the child for whom these supplements are provided. An analysis

    of the Guatemala trial discussed above, found that the youngest children

    were prone to increased diarrhea when receiving supplements but from age

    6 to 36 months the supplements contributed to increased growth but at a

    decreasing rate. After 36 months no impact was observed (Schroeder et al.

    1995). As discussed in the next section, there is also a concern that as a

    child ages past 2 years, addressing malnutrition with the same approaches

    that have an impact at the start of weaning, such as with macronutrientsupplements, may not merely be ineffective, the approach may increase the

    risk of chronic illness when the child is an adult.

    B. The double burden of malnutrition and chronic disease

    Research over the past decade has been able to narrow down the under-

    standing of the periods of nutritional sensitivity and, in doing so, have

    been able to make headway on understanding the biological pathways. In

    doing so, recent research leads to the conclusion that separating the con-

    cept of malnutrition into under- and over-nutrition is, to a fair degree, a

    false dichotomy. For example, given the short duration of the Dutch

    Famine and the rapid return to relative food abundance, this event has

    facilitated a nuanced assessment of critical periods for pregnancy. As men-

    tioned, birth weight was mainly influenced by deprivation in the third

    trimester. However, that measure provides only a limited perspective on

    development, while birth weight is a commonly used indicator for birth

    outcomes it is only one of many aspects of fetal growth linked to

    long-term health. As the children born during and immediately after the

    famine enter into their middle age, it is apparent that they have increased

    risk of chronic disease and some mental illnesses. For example, glucose

    metabolism is impaired in adults who werein uteroat any time during the

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    famine. However, only those adults whose mothers were in their first

    trimester were found to be at increased risk of obesity and heart disease

    as well as breast cancer.5 Recently it was noted that while no cognitive

    impairment was found when the cohort were young adults as they age they

    are showing loss of attention and cognitive ability at a greater rate than

    the general population. Individuals whose mothers were deprived in their

    first trimester show the greatest decline of ability (de Rooij et al. 2010).

    Although the study of the Dutch Famine is the most comprehensive of

    its genre, similar finding have been noted from the disastrous Chinese

    Great Leap and in follow up from the Biafra famine of 19671970. In

    the latter example,Hult et al. (2010)report increased risk of diabetes and

    hypertension among children with fetal exposure to the famine compared

    to the cohorts immediately preceding and immediately following thefamine.

    While famines are extreme events, the patterns uncovered by studying

    these temporally circumscribed events are also found in other analyses.

    Moreover, although the findings are explained in terms of a biological

    model that transcends political boundaries and, indeed, is not specific to

    our species, there is a particular South Asian nuance to the assembled

    data. For example, a longitudinal study of a cohort of births in South

    Delhi followed up to age 32 found that those children who were thinner in

    infancy (with a body mass index [BMI] less than 15) had an acceleratedincrease of BMI until adulthood. Although none were classified as obese

    by age 12, those with the greatest increase in BMI by this age had impaired

    glucose tolerance of diabetes by the age of 32 (Bhargava et al. 2005).

    Similar results have been reported using a panel in Pune (Yajnik 2009).

    While there is not a standard definition for rapid weight gain, the findings

    in the Delhi and Pune studies are in parallel with a systematic global

    review that found a similar association of early rapid growth in infancy

    and early childhood and subsequent obesity in 13 of the 16 studies

    reviewed (Monteiro and Victora 2005).Despite this global patterns there appear to be characteristics of the

    population in India that lead to unique risk factors for chronic disease.

    Moreover, the transition from a resource poor environment to one that is

    less constrained may aggravate these risks. India has not only the largest

    number of malnourished children in the world, but it also has the largest

    number of people with diabetes (Ramachandran and Snehalatha 2010).

    These two statistics may very well have a common origin. While the Indian

    population does not have a high rate of obesity relative to the rest of the

    5 Personal correspondence with T. J. Roseboom based on a presentation in Santiago ChileNovember, 2009, cited with permission.

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    world, or even other Asian countries, there is a tendency to accumulate

    adipose tissue around the waist. This pattern is associated with elevated

    risk of chronic disease. Thus, Indians have been referred to as thin but fat

    (Yajnik 2009). Another risk factor for diabetes largely specific to India islow maternal intake of vitamin B-12 coupled with high folate. Finally, in

    another dimension of intergenerational transmission of health, maternal

    hyperglycemia, or diabetes poses an elevated risk of diabetes for the next

    generation.

    It is largely beyond this article to assess the contribution of these studies

    to rapidly accumulating evidence on chronic disease. But before returning

    to some semblance of economics, it is useful to allude to the larger picture

    that is emerging from such epidemiological results. A seminal paper by

    David Barker and Clive Osmond (1986)in which they traced adult illnessin England and Wales to birth weight records has led to a hypothesis of

    the etiology of diseases termed the fetal origin hypothesis and a body of

    research (as well as a professional association and journal) on the

    development origins of health and disease.Gluckman et al. (2009) have

    placed this in the context of human evolution; contemporary humans

    inhabit an environment very different than that in which they evolved

    leading to a mismatch of their biological programming and the conditions

    in which they live.

    The so-called Barker hypothesis remains controversial, though strongerthan when originally proposed. The original epidemiological results

    showed an association. However, it is difficult, if not impossible, to

    design an experiment that can provide a direct casual interpretation for

    humans within the constraints of research ethics and the practicality of

    observing outcomes that take a generation to manifest.6 Nevertheless, a

    causal interpretation is strengthened when there is an underlying structural

    biological model. Since Barkers and Osmonds original observations in a

    range of laboratory studies using animals has explored this biology

    (Harding et al. 2011), bolstering, if not necessarily proving, the causalinterpretation.

    One manner in which this plays out is illustrated with adaption to stress

    in the womb. The signal derived from limited nutrientsin uteroleads to an

    adaptation in which the child or adult is particularly efficient at conserving

    resources. However, when that individual is subsequently confronted with

    a resource-rich environment, this maladapted response, or mismatch,

    contributes to overnutrition and increased risk of chronic diseases. This

    6 While there are a few randomized nutrition interventions in pregnancy that are beingtracked as the children mature (Fall 2009), even in these rare cases the assignment hasoccurred after the women knew they were pregnant, while laboratory studies of animalsindicate that preconception nutrition is critical.

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    is occasionally referred to as a manifestation of a thrifty gene, but this is

    less genetic than epigenetic. This latter concept refers to a developmental

    plasticity that is not directly driven by genes, but rather a process by which

    parts of the genes or gene-associated proteins are activated usually, but

    not exclusively, through methylation. Thus, genes are not altered, but their

    expression is.7

    Gluckman and Forrester (personal communication) have used the

    concept of fetal plasticity to gain insights on the question of why some

    children manifest undernutrition in the form of marasmus and others with

    kwashiorkor. The former emaciation is less likely to be fatal than the

    mixture of edema and protein breakdown that occurs in the latter.

    Gluckman and Forresters sample of Jamaican survivors of both forms

    of malnutrition proved to be quite different. Whereas children who werediagnosed as marasmic were often born at low weights the other group

    was not. The former group also had a tendency to consume more food

    when given the opportunity. These results can be interpreted as a result of

    fetal nutritional stress that led to low birth weight and an adaption to

    consume resources efficiently consistent with the higher survival reported

    for marasmus relative to kwashiorkor. But, if the child should survive and

    live in an environment replete with resources this risk response is

    mismatched with their current conditions, increasing risks of obesity.

    4 Bringing it all home

    The expanding understanding of biological plasticity and of the link of

    undernutrition and subsequent health risk adds a when to the question of

    what inputs are most critical. It poses some tricky questions for the timing

    of interventions since the maxim, do no harm, is challenged when the

    timing of interventions is considered. In the Indian context, recent

    research indicates that given the possible negative interaction of micronu-

    trients such as vitamin B-12 and folate mentioned above, both of which

    are individually shown to be important, new guidelines may be needed.

    More generally, program design must consider the risk that addressing

    malnutrition in infants and young children may increase illnesses later in

    7 Insights from epigenetics do not require interpretation in terms of evolutionary adapt-ability althoughGluckman et al. (2009) offer a range of plausible hypotheses that tie inthe concept of mismatch. To illustrate, evolutionary theory poses a tradeoff betweenfecundity and longevity. Under stress plants and animals may increase fitness by shiftingresources into early reproduction rather than growth. This may help explain the plasticityof menarche. For example, Indian girls, presumably from a low resource environment,adopted in Sweden, reached puberty earlier than Swedish girls and much earlier than girlsin India.

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    life. Similarly, this understanding provides a perspective to assist in antici-

    pating the public health challenges that emerge in South Asia as it tran-

    sitions from a low income environment.

    Gluckman et al. (2009) poses basic and applied research questions

    that come out of the understanding of developmental plasticity including

    some questions such as what level of risk in later life disease is acceptable

    and what are the social costs of less than optimal development and what

    are the benefitcost ratios of early interventions. These are difficult ques-

    tions and need to be addressed in an interdisciplinary manner but it is

    likely that economists have a comparative advantage in approaching

    them.

    Almond and Currie (2011) have recently taken up the challenge, pla-

    cing the fetal origins hypothesis through an economists lens.8 While theyare in part correct that economics has much to offer, many of the con-

    tributions they illustrate come more from econometrics than from eco-

    nomics, that is, from techniques and data useful for identifying causal

    factors in free living populations more than behavioral choices under

    constraints.

    They do, however, present a general and useful model in which adult

    human capital is produced from inputs in both the prenatal and postnatal

    periods with the relative contribution reflected in the parameter.

    Health AI

    prenatal 1I

    postnatal1=

    This framework has been applied to early child development to explain

    success in schooling and in wage earnings. It has the advantage of illus-

    trating the potential for inputs in one period to complement the invest-

    ments in another. Similarly, this model provides a tool for understanding

    the manner by which a short-term income shock affects outcomes among

    siblings. It also helps frame some key questions. Economists rightfully ask

    the manner in which preferences for intra-household equity affect how

    much will be invested in a child in a subsequent period. This has direct

    applicability to postnatal investment in nutrition and child stimulation.

    They also can give a perspective on how much investment may be needed

    to reduce the impact of a bad draw in either period from having serious

    consequences for the prospects of the next generation.

    In modeling the production of health, conventionally one looks for the

    demand for inputs given prices and preferences or uses a reduced form. In

    either case, endowments are generally taken as endogenous. The fetal

    8 The disciplines may not be as distinct as they first appear. Both economics and evolu-tionary biology are about resource scarcity; both tribes consider Malthus among theirancestors.

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    origin hypothesis and the recognition of epigenetics move the question

    back, at least to pregnancy and perhaps to the preconception period.

    Practically speaking, the implies an increased focus on adolescent health

    and, as Almond and Currie put it, a the need for a greater understandingof what is the cost of an ounce of prevention relative to a pound of cure

    (properly discounted, of course).

    Gluckman et al. (2009)also argued that the issue of plasticity implies

    that the focus on birth weight may miss major indicators that point to later

    health risks. Expanding on this theme, as progress is made on reducing

    child mortality, emphasis on anthropometric indicators may be too narrow

    a guide to health programs. First, while the Millennium Development

    Goal indicator of weight for age has the advantage of relative ease in

    monitoring, rapid weight gain after infancy may have attendant risks.Moreover, once the period of greatest risk for child mortality is passed,

    neither weight nor height is the direct measure of what is most important

    for public policy. As implied in Almond and Currie (2010, 2011), the

    outcome of interest in the inter-period model in equation 1 is human

    capital more broadly defined than physical growth alone.

    More than the implication of maternal height or hyperglycemia for the

    intergenerational transmission of poverty, one is concerned about a wider

    range of cognitive and non-cognitive measures to address inequality. As

    Naudeau et al. (2011)point out, there is a sharp wealth gradient in thesemeasures in which the gap between children from poorer households and

    wealthier households widens over the same time period that is the period

    of focus for most nutrition interventions. As the human capital model gets

    more nuanced in terms of time indexed inputs, the range of indicators of

    development should be correspondingly more nuanced as well.

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