Does India Really Suffer from Worse Child Malnutrition Than Sub-Saharan Africa?

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SPECIAL ARTICLE

Does India Really Suffer from Worse Child
Malnutrition Than Sub-Saharan Africa?

Arvind Panagariya

A common continuing criticism of the economic reforms                     1 Introduction

                                                                          A
in India has been that despite accelerated growth and                               widely-held view among nearly all development ex-
                                                                                    perts familiar with India is that despite accelerated
all-around poverty reduction, the country continues to
                                                                                    growth in the last three decades, the country contin-
suffer from worse child malnutrition than nearly all                      ues to suffer from worse child malnutrition than virtually
Sub-Saharan African countries with lower per capita                       every Sub-Saharan African country with lower per capita
incomes. This paper argues that this narrative, nearly                    income. According to this view, India is also guilty of having
                                                                          made little or no progress in bringing malnutrition levels down.
universally accepted around the world, is false. It is the
                                                                          A prominent example is The Economist magazine, which stated
artefact of a faulty methodology that the World Health                    in an article in its 23 September 2010 edition, “Nearly half of
Organisation has pushed and the United Nations has                        India’s small children are malnourished: one of the highest
supported. If appropriate corrections are applied, in all                 rates of underweight children in the world, higher than most
                                                                          countries in sub-Saharan Africa. More than one-third of the
likelihood, India will be found to be ahead of
                                                                          world’s 150m malnourished under-fives live in India.”
Sub-Saharan Africa in child malnutrition, just as in other                   In addition to this high level of child malnutrition, the article
vital health indicators.                                                  repeated the common claim that India had made very little
                                                                          progress in combating child malnutrition. It noted, “Almost as
                                                                          shocking as the prevalence of malnutrition in India is the coun-
                                                                          try’s failure to reduce it much, despite rapid growth. Since 1991
                                                                          gross domestic product (GDP) has more than doubled, while
                                                                          malnutrition has decreased by only a few percentage points.”
                                                                          In January 2011, even India’s otherwise measured Prime Minis-
                                                                          ter Manmohan Singh went on to lament, “The problem of mal-
                                                                          nutrition is a matter of national shame”, while releasing the
                                                                          much publicised Hunger and Malnutrition (HUNGaMA) Report.1
                                                                             Reforms critics had originally argued that reforms had not
                                                                          helped the poor or that they had left the socially disadvan-
                                                                          taged groups behind. Evidence has now decisively established,
                                                                          however, that reforms have been accompanied by a decline in
                                                                          poverty across all social groups, including the scheduled
                                                                          castes and scheduled tribes (Mukim and Panagariya 2012),
                                                                          that this reduction accelerated between 2004-05 and 2009-10
                                                                          with the acceleration in growth, and that the gap in poverty
I thank an anonymous referee of this journal for several helpful          ratios between the socially disadvantaged and other groups
suggestions. The paper has also benefited from criticism by Jishnu Das,   has finally begun to come down (Thorat and Dubey 2012; Pan-
numerous very helpful discussions with Reuben Abraham and                 agariya and Mukim 2013). Therefore, the critics have now
comments by Prashant Reddy, Ursula Schwartzhaupt, and Rajitha
                                                                          shifted to arguing that the reforms have done precious little
Swaminathan. It was the basis of the Chandrasekaran Memorial Lecture
at the International Institute of Population Studies, Mumbai, on          for India’s malnourished children, with the country lagging
8 November 2012, and was originally written for the Program on Indian     behind even much poorer Sub-Saharan Africa.
Economic Policies at Columbia University, funded by a grant from the         This paper rejects this claim, arguing that it stems from
John Templeton Foundation. The opinions expressed do not necessarily      child malnutrition estimates based on a flawed measurement
reflect the views of the John Templeton Foundation.
                                                                          methodology. The central problem with the current methodo-
Arvind Panagariya (ap2231@columbia.edu) is Professor of Economics         logy is the use of common height and weight standards around
and the Jagdish Bhagwati Professor of Indian Political Economy at         the world to determine malnourishment, regardless of differ-
Columbia University, the United States.
                                                                          ences that may arise from genetic, environmental, cultural,
98                                                                                  may 4, 2013   vol xlviiI no 18   EPW   Economic & Political Weekly
SPECIAL ARTICLE

and geographical factors. Though medical literature recog-               prominently in poor gains in height, weight, and circumfer-
nises the importance of these factors, the World Health                  ences of head and mid-upper arm. Other physical symptoms
Organisation (WHO) totally ignores them when recommending                such as skin peeling, abdominal distension, liver enlargement,
globally uniform height and weight cut-off points against                and sparse hair as well as behavioural characteristics such as
which children are compared to determine whether they suf-               anxiety, irritation, and attention deficit may also accompany
fer from stunting (low height for age) or underweight (low               protein deficiency. Micronutrient deficiency results from inad-
weight for age) problems.                                                equate levels of iron, folate, iodine, and various vitamins,
   It is important to point out at the outset that it is not my inten-   including A, B6, D, and E, in the body. These deficiencies lead
tion to downplay the seriousness of the child malnutrition prob-         to anaemia, goitre, bone deformities, and night blindness.
lem in India. Just like vital health statistics such as life expect-        Given these many dimensions involved in identifying mal-
ancy, infant and child mortality rates, and maternal mortality           nutrition, only a thorough medical check-up can properly de-
ratio, which need continued improvement, child malnutrition              termine whether a child is malnourished or not. But few glo-
must be brought down and eliminated. The contention of the               bally comparable large-scale surveys rely on extensive medical
paper, instead, is that the current globally uniform height- and         check-ups to measure malnutrition in children. Moreover,
weight-based measures of child malnutrition, which place India           after the United Nations (UN) introduced the Millennium
behind nearly every Sub-Saharan African country, are premised            Development Goals (MDGs), which prominently included rapid
on invalid assumptions and therefore need correction. The                progress in combating child malnutrition as a key goal, the
paper proposes to establish a strong presumption that once               pressure to come up with estimates of the proportion of chil-
health experts and economists come together to devise a better           dren suffering from malnutrition grew. As a result, height and
methodology of measurement, we will find that as in the case of          weight, which are easy to measure and require no specialised
indicators such as life expectancy, infant and child mortality           medical skills, quickly became the focus of attention. This was
rates, and maternal mortality ratio, India does not suffer worse         further aided by the WHO, which provided the common stand-
child malnutrition than Sub-Saharan Africa.                              ards of height and weight by age and gender to determine
   Some may argue that the debate on precisely measuring                 whether a child was stunted or underweight.
child malnutrition is counterproductive since as long as a large            Today, virtually all headline figures on child malnutrition,
proportion of children are malnourished, the effort required             including the ones that led Manmohan Singh to declare the
to combat it is the same regardless of precise numbers.2 There           phenomenon a national shame, are based on height and
are at least three objections to this argument. First, proper            weight. In view of the multidimensional nature of child mal-
measurement and determination of progress have serious im-               nutrition, this singular focus on low height and weight should
plications for the allocation of scarce revenue resources among          itself be a source of concern. But this is not the direction of the
competing social objectives, especially in a poor country with           critique in this paper.
limited revenues. Should more be spent on combating child                   Instead, the focus of the paper is narrower. Accepting poor
malnutrition or on improving elementary education? Or on                 height and weight gains as the principal manifestations of
providing guaranteed employment or on alleviating adult                  malnutrition, I ask whether we are correctly identifying stunted
hunger? Should India spread the limited resources available              and underweight children. The underlying question is about
for combating child malnutrition over half its children or just a        the validity of applying uniform height and weight norms
quarter of them? These are real choices a poor country must              around the world as the basis for determining whether a given
make in setting its budget priorities.                                   child is well-nourished or malnourished. This focus does not
   Second, if a child is already receiving a balanced diet but is        deny in any way the importance of a full medical examination
misclassified as malnourished because an erroneous standard is           to determine whether or not a child is malnourished.3 But it
applied to evaluate her nutrition status, we may prescribe an in-        addresses the deficiencies of the measures that are the source
crease in her diet when such an adjustment is uncalled for. At the       of virtually all discussion on child malnutrition in the public
other extreme, what if we misclassify a malnourished child as            policy space.
well-nourished? In the former case, we run the risk of turning a
healthy child obese and in the latter that of ignoring malnutrition.     3 Planting the Seeds of Doubt
   Finally, truth in numbers is an essential element in serious          I begin by offering three comparisons that challenge the nar-
intellectual discourse. Else, we would be tempted to falsify all         rative that India has more stunted and underweight children
other indicators such as those relating to poverty alleviation,          than Sub-Saharan Africa. These comparisons should at least
growth, life expectancy, infant mortality rate, and maternal             give the reader a reason to pause and entertain the possibility
mortality ratio on the premise that this is an effective way of          that something is wrong with the news headlines depicting
attracting public attention. The ends rarely justify the means.          India as one of the worst performers in child nutrition.

2 Why the Focus on Height and Weight                                     3.1 India versus Chad
Malnutrition is a multidimensional phenomenon. In broad                  Let us begin by comparing a set of commonly-used health indi-
terms, it may be divided into protein energy malnutrition and            ces for the child and the mother in India to those in Chad and
micronutrient deficiency. The former manifests itself most               the Central African Republic, two of the poorest countries in
Economic & Political Weekly   EPW   may 4, 2013   vol xlviiI no 18                                                                       99
SPECIAL ARTICLE

the world. As Table 1 shows, Chad has just 48 years of life ex-                                    3.3 India versus the 33 Poorer Sub-Saharan African
pectancy against India’s 65 years; an infant mortality rate                                        Countries
(IMR) of 124 against India’s 50; an under-five mortality rate of                                      The above comparisons are not isolated examples. A comparison of
Table 1: India Compared to Chad and Central African Republic                                                        India with nearly every one of the 33 Sub-Saharan
Indicator                                                  India       Chad   Central African   Chad as % CAR as %  African countries with lower per capita incomes in
                                                                              Republic (CAR)     of India  of India
                                                                                                                    2009 in current dollars shows the same pattern. I
Life expectancy (2009)                              65                   48         48               74        74
Infant mortality per 1,000 live births (2009)       50                  124        112            248       224
                                                                                                                    demonstrate this with the help of Figures 1-3 and
Under-five mortality per 1,000 live births          66                  209        171            317       259     Figures 4-7 (p 101) with each figure comparing India
Maternal mortality per 1,00,000 live births (2009) 230                1,200       850             522       370     to the 33 countries in Sub-Saharan Africa along one
Per cent children below 5 stunted (2000-09)        47.9                44.8       44.6              94         93   health indicator. I arrange the countries from left to
Per cent children below 5 underweight (2000-09) 43.5                   33.9       21.8              78         50
                                                                                                                    right in order of increasing per capita incomes.
Source: WHO World Health Statistics, 2011.
                                                                                                   Figure 1: Life Expectancy in India and 33 Poorer Sub-Saharan African Countries
209 relative to India’s 66; and a maternal mortality ratio                                         Life Expectancy (2009): Countries in Rising Order of Per Capita GDP from L to R
                                                                                                    70
(MMR) of 1,200 compared to India’s 230. Yet, Chad has dispro-                                                                66
                                                                                                                                      60 59 65                 59     60 57 6058 62 60
                                                                                                                                                                                                65
                                                                                                    60        56          57
portionately fewer stunted and underweight children than                                                50 49       49
                                                                                                                       54       52 49
                                                                                                                                              48 48 52 49 52 49 48
                                                                                                                                                                   53
                                                                                                                                                                           48
                                                                                                                                                                                55     50 54 51
                                                                                                    50           47
India. The comparison with the Central African Republic is
                                                                                                    40
equally stark.                                                                                      30
                                                                                                    20
3.2 Kerala versus Senegal and Mauritania
                                                                                                    10
Next, I compare the Indian state of Kerala with two other coun-                                      0

                                                                                                                           Liberia

                                                                                                                    Madagascar

                                                                                                                   Burkina Faso

                                                                                                                      Mauritania
                                                                                                                         Burundi
                                                                                                           Dem Rep of Congo

                                                                                                                          Malawi
                                                                                                                   Sierra Leone
                                                                                                                         Ethiopia
                                                                                                                            Niger
                                                                                                                           Eritera
                                                                                                                          Guinea
                                                                                                                  Mozambique

                                                                                                                             Chad
                                                                                                                   Gambia, The
                                                                                                                             Togo

                                                                                                                          Zambia
                                                                                                         Central African Republic

                                                                                                                      Zimbabwe

                                                                                                                 Guinea-Bissau

                                                                                                                          Nigeria
                                                                                                                         Uganda

                                                                                                                         Rwanda

                                                                                                                              Mali
                                                                                                                            Kenya
                                                                                                                            Benin
                                                                                                                         Lesotho
                                                                                                                        Comoros

                                                                                                                        Tanzania
                                                                                                                         Senegal
                                                                                                                           Ghana
                                                                                                                   Cote d’lvoire

                                                                                                                      Cameroon
                                                                                                                             India
tries from Sub-Saharan Africa, Senegal and Mauritania. Of the
28 states in India, I choose Kerala to bring out the absurdity of
the current child malnutrition indicators as sharply as possible.
The conventional vital health statistics in Kerala are the high-
est among all Indian states and rival those observed in China.                                     Figure 2: Infant Mortality Rates in India and 33 Poorer Sub-Saharan
Among the largest 17 Indian states, it ranks fourth in terms of                                    African Countries
                                                                                                   Infant Mortality per 1,000 Live Births (2009): Rising Per Capita GDP from L to R
per capita income. In terms of per capita income, Senegal and                                      140
                                                                                                          126      123                                             124
Mauritania are among the better-off countries in Sub-Saharan                                       120
                                                                                                                                                112
                                                                                                                                                            115
                                                                                                   100 101                                                            101
Africa but both lag behind India and Kerala with the gap being                                      80
                                                                                                                                 96
                                                                                                                                                         91
                                                                                                                                                                                                    95
                                                                                                                              88             86                                               83 86
especially large with respect to the latter.4                                                       60
                                                                                                              80        76          78             79                       75 75
                                                                                                                                                                                  74 68
                                                                                                                 69 67                                          70
Table 2: Comparing Kerala in India with Senegal and Mauritania in Africa                            50                                 64             56                  55 61
                                                                                                    40                                                                                  51 47          50
Indicator                                                    Kerala     Senegal    Mauritania                              39             40
                                                                                                    30
Life expectancy                                                74          62           58          20
Infant mortality per 1,000 live births                         12          51           74           10
Under-five mortality per 1,000 live births*                    16          93          117            0
                                                                                                                          Burundi
                                                                                                            Dem Rep of Congo
                                                                                                                            Liberia
                                                                                                                           Malawi
                                                                                                                     Sierra Leone
                                                                                                                          Ethiopia
                                                                                                                             Niger
                                                                                                                            Eritera
                                                                                                                           Guinea
                                                                                                                   Mozambique
                                                                                                                     Gambia,The
                                                                                                                              Togo
                                                                                                                     Madagascar
                                                                                                                           Zambia
                                                                                                         Central African Republic
                                                                                                                          Uganda
                                                                                                                      Zimbabwe
                                                                                                                    Burkina Faso
                                                                                                                  Guinea-Bissau
                                                                                                                          Rwanda
                                                                                                                              Chad
                                                                                                                               Mali
                                                                                                                             Kenya
                                                                                                                             Benin
                                                                                                                          Lesotho
                                                                                                                        Comoros
                                                                                                                      Mauritania
                                                                                                                         Tanzania
                                                                                                                          Senegal

                                                                                                                           Nigeria
                                                                                                                            Ghana
                                                                                                                     Cote d’lvoire

                                                                                                                       Cameroon
                                                                                                                              India
Maternal mortality per 1,00,000 live births (2009)            95          410         550
Per cent children below 5 stunted (2000-09)                  25.0        20.0         24.2
Per cent children below 5 underweight (2000-09)              23.0        15.0         16.7
Source: National Family Health Survey 2005-06 for Kerala. All other data from World Health
Statistics, 2011.

   According to Table 2, Senegal, which has 4.25 times the in-                                     Figure 3: Under-Five Mortality Rates in India and 33 Poorer Sub-Saharan
                                                                                                   African Countries
fant mortality rate of Kerala, almost six times Kerala’s under-                                    Under-Five Mortality per 1,000 Live Births (2009): Rising Per Capita GDP from L to R
                                                                                                   250
five mortality, and 4.3 times Kerala’s maternal mortality ratio,
                                                                                                           199                                                                     209
                                                                                                                                                                             193      191
has lower rates of stunting and underweight children. Chil-                                        200              192
                                                                                                         166                                              171
                                                                                                                          160                                              166
dren in Senegal, better nourished as per malnutrition esti-                                                                          142142             141                                                                       138154
                                                                                                   150
mates, die at rates many times those in Kerala. A comparison                                                   112110
                                                                                                                                                                128
                                                                                                                                                                                 111             118           117              118
                                                                                                                        104               103 98                                                            104 108
                                                                                                                                                                                                                      93
with Mauritania yields the same picture.                                                           100                                                                89                    84         84
                                                                                                                                                                                                                           69              66
                                                                                                                                                   58
   A higher incidence of child malnutrition in Kerala than                                          50
                                                                                                                                55

Senegal and Mauritania is even more puzzling given its signifi-
cantly higher female literacy rate. The state has had a long his-                                    0
                                                                                                                         Burundi

                                                                                                                           Liberia
                                                                                                            Dem Rep of Congo

                                                                                                                          Malawi
                                                                                                                    Sierra Leone
                                                                                                                         Ethiopia
                                                                                                                            Niger
                                                                                                                           Eritera
                                                                                                                          Guinea
                                                                                                                  Mozambique
                                                                                                                    Gambia,The
                                                                                                                             Togo
                                                                                                                    Madagascar
                                                                                                                          Zambia
                                                                                                         Central African Republic
                                                                                                                         Uganda
                                                                                                                      Zimbabwe
                                                                                                                    Burkina Faso
                                                                                                                 Guinea-Bissau
                                                                                                                         Rwanda
                                                                                                                             Chad
                                                                                                                              Mali
                                                                                                                            Kenya
                                                                                                                            Benin
                                                                                                                         Lesotho
                                                                                                                        Comoros
                                                                                                                      Mauritania
                                                                                                                        Tanzania
                                                                                                                         Senegal
                                                                                                                           Ghana
                                                                                                                    Cote d’lvoire
                                                                                                                          Nigeria
                                                                                                                      Cameroon
                                                                                                                             India

tory of educating its women and its female literacy rate at 92%
in 2011 is among the highest in the developing world. In addi-
tion, women have traditionally enjoyed high social status in
Kerala with many communities following the matrilineal tra-
dition. In contrast, at 29%, Senegal has one of the lowest                                           The life expectancy at birth in India at 65 exceeds those in all
female literacy rates in the world. Mauritania does better at                                      but two of the 33 Sub-Saharan African countries (at 66 years,
51%, but it also lags far behind Kerala.                                                           Eritrea edges out India, while at 65 Madagascar ties with it).
100                                                                                                                     may 4, 2013           vol xlviiI no 18                     EPW       Economic & Political Weekly
SPECIAL ARTICLE
Figure 4: Still Birth Rates in India and 33 Poorer Sub-Saharan                                                                                                                                     The maternal mortality ratio per 1,00,000 live births in In-
African Countries
Stillbirth per 1,000 Live Births (2009): Rising Per Capita GDP from L to R
                                                                                                                                                                                                dia at 230 is lower than those in every one of the 33 Sub-Saha-
 45                                                                                                                                                                         42                  ran African countries.
40
                                                                                                                                                           34
                                                                                                                                                                                                   But this pattern collapses when it comes to child malnutrition.
35
30           29               30                                                                       30        29                                                                                The proportion of children under five years of age classified as
       28         27               26                   28 26                   26                26                                27 27 26                           27           26
                                                              25                     24 25                                    24 25
25                     24                 23
                                                21
                                                     24
                                                                           21
                                                                                                            23
                                                                                                                      23 22                                       22                      22
                                                                                                                                                                                                stunted (low height for age) at 47.9% is higher in India than all but
                                                                                             20
20                                                                                                                                                                                              six of the poorer Sub-Saharan African countries (Burundi, Malawi,
15                                                                                                                                                                                              Ethiopia, Niger, Madagascar, and Rwanda have stunting rates of
10
                                                                                                                                                                                                63.1%, 53.2%, 50.7%, 54.8%, 49.2% and 51.7%, respectively).
  5
  0                                                                                                                                                                                             Figure 7: Percentage of Children Underweight in India and 33 Poorer
                        Liberia

                 Madagascar

                Burkina Faso

                   Mauritania
                      Burundi
         Dem Rep of Congo

                       Malawi
                 Sierra Leone
                      Ethiopia
                         Niger
                        Eritera
                       Guinea
               Mozambique
                Gambia, The
                          Togo

                       Zambia
       Central African Republic

                   Zimbabwe

              Guinea-Bissau
                      Rwanda
                          Chad

                Cote d’lvoire
                       Nigeria
                   Cameroon
                          India
                      Uganda

                           Mali
                         Kenya
                         Benin
                      Lesotho
                     Comoros

                     Tanzania
                      Senegal
                        Ghana
                                                                                                                                                                                                Sub-Saharan African Countries
                                                                                                                                                                                                Percentage of Children below 5 Underweight (2000-09): Rising Per Capita GDP from L to R
                                                                                                                                                                                                 50
                                                                                                                                                                                                                                                                                                       43.5
                                                                                                                                                                                                 45
                                                                                                                                                                                                    38.9                 39.9
                                                                                                                                                                                                40                                                       37.4
                                                                                                                                                                                                                     34.6 34.5                                    33.9
                                                                                                                                                                                                 35
                                                                                                                                                                                                         28.2
Figure 5: Maternal Mortality Ratio in India and 33 Poorer Sub-Saharan                                                                                                                            30                                                                   27.9                        26.7
                                                                                                                                                                                                                                                                                   25.0
African Countries                                                                                                                                                                               25          20.4 21.3             21.2 20.5     21.8
                                                                                                                                                                                                                                                              18.0            20.2
                                                                                                                                                                                                                               20.8                                                     16.7
Maternal Mortality per 1,00,000 Live Births (2009): Rising Per Capita GDP from L to R                                                                                                            20             15.5                  15.8  14.9 16.414.0 17.4            16.4 16.6 16.7 14.5 16.7 16.6
                                                                                                                                                                                                                                                                                             14.3
1400                                                                                                                                                                                             15
                                                                                                                 1200
1200                                                                                                                                                                                             10
          970         990      970                                                                     1000                                                                                       5
1000
                                           820                                       850                              830                                                                         0

                                                                                                                                                                                                                       Liberia

                                                                                                                                                                                                                Madagascar

                                                                                                                                                                                                                Burkina Faso

                                                                                                                                                                                                                  Mauritania
                                                                                                                                                                                                                     Burundi
                                                                                                                                                                                                        Dem Rep of Congo

                                                                                                                                                                                                                      Malawi
                                                                                                                                                                                                                Sierra Leone
                                                                                                                                                                                                                     Ethiopia
                                                                                                                                                                                                                        Niger
                                                                                                                                                                                                                       Eritera
                                                                                                                                                                                                                      Guinea
                                                                                                                                                                                                              Mozambique

                                                                                                                                                                                                     Central African Republic
                                                                                                                                                                                                                Gambia, The
                                                                                                                                                                                                                         Togo

                                                                                                                                                                                                                      Zambia

                                                                                                                                                                                                                  Zimbabwe

                                                                                                                                                                                                             Guinea-Bissau
                                                                                                                                                                                                                     Rwanda
                                                                                                                                                                                                                         Chad

                                                                                                                                                                                                                    Tanzania
                                                                                                                                                                                                                     Uganda

                                                                                                                                                                                                                          Mali
                                                                                                                                                                                                                        Kenya
                                                                                                                                                                                                                        Benin
                                                                                                                                                                                                                     Lesotho
                                                                                                                                                                                                                    Comoros

                                                                                                                                                                                                                     Senegal
                                                                                                                                                                                                                       Ghana
                                                                                                                                                                                                               Cote d’lvoire
                                                                                                                                                                                                                      Nigeria
                                                                                                                                                                                                                  Cameroon
                                                                                                                                                                                                                         India
                                                                                             790                                                      790                     840
 800
                670                                    680
                                                             550                                   560      540             530         530     550                                 600
 600                        510      470                                  470                                                                                           470
                                                               400 350440                  430                                    410
                                                                                                                                              340
                                                                                                                                                            410
                                                                                                                                                                  350
 400
                                                 280
                                                                                                                                                                                         230
 200
      0
                                                                                                                                                                                                  The proportion of children under five years of age classified as
                           Liberia

                    Madagascar

                    Burkina Faso

                      Mauritania
                         Burundi
            Dem Rep of Congo

                          Malawi
                    Sierra Leone
                         Ethiopia
                            Niger
                           Eritera
                           Guinea
                   Mozambique
                    Gambia, The
                             Togo

                          Zambia
          Central African Republic

                      Zimbabwe

                  Guinea-Bissau
                         Rwanda
                             Chad

                        Tanzania
                         Uganda

                              Mali
                            Kenya
                            Benin
                         Lesotho
                        Comoros

                         Senegal
                           Ghana
                   Cote d’lvoire
                          Nigeria
                       Cameroon
                             India

                                                                                                                                                                                                underweight (low weight for age) at 43.5% is higher in India
                                                                                                                                                                                                than every one of the 33 poorer Sub-Saharan African countries.

                                                                                                                                                                                                4 Can Superior Health Infrastructure Be the Explanation?
Figure 6: Percentage of Children Stunted in India and 33 Poorer Sub-Saharan                                                                                                                     When confronted with this evidence, some proponents of the
African Countries                                                                                                                                                                               current malnutrition indicators respond that the lower rates of
Percentage of Children below 5 Stunted (2000-09)
70                                                                                                                                                                                              infant and child mortality in India relative to Sub-Saharan
      63.1
60
                       53.2              54.8
                                                                                                  51.7
                                                                                                                                                                                                Africa despite higher malnutrition rates reflect its superior
                                  50.7              47.0               49.2                   47.7                        46.9                                                           47.9
50        45.8                              43.7                          45.8 44.6       44.5        44.8       44.7 45.2                          44.4                                        medical infrastructure. In addition to contributing to low mor-
              39.4                              40.0                                                                                                                        41.0
                                                                                                                                                                   40.1
40                          37.4                                                  38.7
                                                                                      35.8
                                                                                                          38.5
                                                                                                              35.2                                                               36.4           tality rates, the latter contributes to increased malnutrition by
30
                                                             27.6
                                                                    26.9                                                                       24.2
                                                                                                                                                                28.6                            helping save many malnourished infants and children.5
20
                                                                                                                                                        20.1                                       Though a logical possibility, the authors of this explanation
10
                                                                                                                                                                                                provide no concrete empirical evidence in its support. What-
                                                                                                                                                                                                ever evidence we can gather points in the opposite direction –
 0
                                                                                                                                                                                                countries that have better medical infrastructure also nourish
                        Liberia

                 Madagascar

                 Burkina Faso

                   Mauritania
                      Burundi
         Dem Rep of Congo

                       Malawi
                 Sierra Leone
                      Ethiopia
                         Niger

      Central African Republic
                        Eritera
                       Guinea
               Mozambique
                 Gambia, The
                          Togo

                       Zambia

                   Zimbabwe

              Guinea-Bissau
                      Uganda

                      Rwanda
                          Chad
                           Mali
                         Kenya
                         Benin
                      Lesotho
                     Comoros

                     Tanzania
                      Senegal
                        Ghana
                Cote d’lvoire
                       Nigeria
                   Cameroon
                          India

                                                                                                                                                                                                Figure 8: Malnutrition among Children Above 1 and Below 5 Years in Rural
                                                                                                                                                                                                Areas of Nine States under the NCHS 1977 Standard
                                                                                                                                                                                                90
                                                                                                                                                                                                                77                                                79
                                                                                                                                                                                                80

                                                                                                                                                                                                70                      69
   The infant mortality rate per 1,000 live births in India at 50                                                                                                                                                                62
                                                                                                                                                                                                                                                                            65
                                                                                                                                                                                                                                                                                     58
is lower than those in all but three of the 33 Sub-Saharan Afri-                                                                                                                                60
                                                                                                                                                                                                                                           55
                                                                                                                                                                                                                                                                                               52
can countries (Eritrea, Madagascar, and Ghana have infant                                                                                                                                       50
mortality rates of 39, 40, and 47, respectively).                                                                                                                                               40
   The under-five mortality rate per 1,000 live births in India at
                                                                                                                                                                                                30
66 is lower than those in all but two of the 33 Sub-Saharan
African countries (Eritrea and Madagascar have under-five                                                                                                                                       20

mortality rates of 55 and 58, respectively).                                                                                                                                                    10
   The stillbirth rate per 1,000 births at 22 in India is lower                                                                                                                                  0         1975-79 1988-90 1996-97 2003-06                    1975-79 1988-90 1996-97 2003-06
than those in all but five of the 33 Sub-Saharan African coun-                                                                                                                                        Proportion of 1-5 years old children underweight      Proportion of 1-5 years old children stunted
tries (Eritrea, Madagascar, Zimbabwe, Kenya, and Ghana have                                                                                                                                     Source: Authors’ construction based on NNMB (1999), Report of Second Repeat Survey-Rural,
                                                                                                                                                                                                Indian Council of Medical Research, Hyderabad, Table 19 and NNMB Fact Sheet 2003-06 at
stillbirth rates of 21, 21, 20, 22 and 22, respectively).                                                                                                                                       http://www.nnmbindia.org/downloads.html (accessed 27 June 2011).

Economic & Political Weekly                                         EPW              may 4, 2013                  vol xlviiI no 18                                                                                                                                                                    101
SPECIAL ARTICLE

their children better. Developed countries enjoy low infant                                                                                    Three important indicators of child malnutrition are con-
and child mortality rates and maternal mortality ratios as well                                                                             ventionally reported – the proportion of children stunted,
as low rates of malnutrition. Likewise, declining rates of mal-                                                                             those underweight, and those wasted. As already indicated,
nutrition typically accompany declining infant and child mor-                                                                               stunting and underweight refer to low height and low weight
tality rates and maternal mortality ratios.                                                                                                 for age, respectively. Wasting refers to low weight for a given
   India’s own experience points to improving malnutrition side-                                                                            height, regardless of age. Since wasting has received little at-
by-side with declining infant and child mortality rates and ma-                                                                             tention in the public policy discourse, I will not focus on it in
ternal mortality ratio. Figure 8 (p 101) shows the average pro-                                                                             the rest of this paper.6 Indeed, for clarity of exposition, I will
portions of children classified as underweight and stunted in                                                                               present much of the discussion with respect to the determina-
rural areas in nine Indian states during four time periods – 1975-                                                                          tion of stunting.
79, 1988-90, 1996-97, and 2003-06. The proportion of children                                                                                  The height of an individual can vary for both genetic and
stunted as well as those underweight declines between every                                                                                 nutritional reasons. Without detailed medical examination,
successive pair of periods. If improved ability to save the infants                                                                         one cannot conclude whether an individual is short because of
and children as reflected in declining infant and child mortality                                                                           malnourishment or because of genetic factors. This makes
rates are expected to result in worsening child nutrition per-                                                                              identifying stunting by referring to just height, an imperfect
formance, we should observe rising rates of malnutrition in                                                                                 exercise. Nevertheless, this is the current practice.
India. But we see precisely the opposite trend in Figure 8.                                                                                    The current approach is to define a height norm for children
  Figure 9: Child Malnutrition and Under-Five Mortality                                                                                     of a given age and gender. All children of the same age and
                                                         70                                                                                 gender in a given population who are below this norm are
                                                                                                  Percentage of under-five children         classified as stunted. The critical remaining step then is to
                                                                                                       underweight, 2005-06
                                                                                                                                            identify the height norm. In the strictest sense, the norms cur-
                                                         60
                                                                                                                                            rently used are premised on the following key assumption – all
Percentage of children under-five underweight, 2005-06

                                                                                                                                            differences in height between populations of children of a
                                                         50
                                                                                                                                            given age and sex occur due to differences in nutrition.
                                                                                        y = 0.3024X + 19.145
                                                                                            R2 = 0.51528                                       This assumption implies that populations of children from
                                                                                                                                            entirely different races, ethnicities, cultures, time periods, and
                                                         40                                                                                 geographical locations would look identical in terms of height
                                                                                                                                            distribution provided they are given the same nutrition. That
                                                                                                                                            is to say no differences in the proportion of children below or
                                                         30                                                                                 above any pre-specified height would exist between two popu-
                                                                                                                                            lations provided they are given identical nutrition.
                                                                    Linear (Percentage of under-five children                                  Suppose we can identify the population of children of a
                                                         20
                                                                             under weight, 2005-06)                                         given age and sex that is the healthiest possible. Although the
                                                                                                                                            heights of children within this population will differ due to
                                                         10                                                                                 genetic differences, taken as a whole, the population repre-
                                                                                                                                            sents the best attainable distribution. Then, given the above
                                                                                                                                            assumption, any deviations in the distribution of height in a
                                                          0                                                                                 population of children of the same age and sex from this popu-
                                                              0   20           40             60            80           100          120
                                                                  Under-five mortality per thousand live births, 2001-05                    lation would be attributable to malnutrition. This is the es-
  Source: Author’s construction based on data from National Family Health Survey 2005-06.                                                   sence of the approach underlying the measures of malnutri-
   This same pattern is observed in cross-state data in India.                                                                              tion currently in use worldwide.
Figure 9, which shows the proportion of children who are un-                                                                                   The first step in making the approach operational is to iden-
derweight against the under-five mortality rate in the 15 larg-                                                                             tify the healthiest populations of boys and girls of different
est states, illustrates this graphically. The scatter-plot shows an                                                                         ages. Once this is accomplished, a certain percentage of chil-
upward trend, which is confirmed by the fitted linear trend.                                                                                dren at the bottom of the distribution of this population by
On average, states with superior outcomes in child mortality                                                                                height are defined as stunted. Based on statistical considera-
rates also exhibit superior nutrition outcomes. Once again, on                                                                              tions, the conventional cut-off point for this purpose is set at
average, a greater ability to save infants and children does not                                                                            2.3%.7 The height of the child at the 2.3 percentile in the
translate into a higher incidence of malnutrition.                                                                                          healthiest population serves as the norm against which all
                                                                                                                                            children are measured to determine their stunting status.
5 Measuring Malnutrition: Methodology                                                                                                          What is required then is the identification of the healthiest
It is the contention of this paper that the answer to the para-                                                                             population of children of a given age and gender, or what is often
doxical behaviour of stunting and underweight indicators in                                                                                 called the “reference population”. The US first adopted such a
the India-Sub-Saharan Africa comparison lies in the method-                                                                                 reference population in 1977. The National Center for Health
ology underlying the measurement of these indicators. This is                                                                               Statistics (NCHS) of the Centers for Disease Control (CDC) deve-
what is developed and defended in the rest of this paper.                                                                                   loped the height and weight distributions of children by age
102                                                                                                                                                    may 4, 2013   vol xlviiI no 18   EPW   Economic & Political Weekly
SPECIAL ARTICLE

and sex using longitudinal data collected in Yellow Springs,            Figure 10: Hypothetical Cumulative Height Distributions of Five-Year-Old
                                                                        Boys in India
Ohio between 1929 and 1975 by the Fels Research Institute               Percentage of population
(Roche 1992). The NCHS 1977 distributions remained in use to            below the height shown
measure malnutrition among children in the US until 2000.
Beginning in the late 1970s, the WHO encouraged other countries
to adopt this same reference population to measure malnutrition.                                    Distribution 1       Distribution 2
   In the 1990s, the CDC concluded that the Fels reference pop-          100
ulation data came from a sample that was quite limited in
genetic, geographic, cultural, and socio-economic variability                                                                      Distribution 3
(Kuczmarski et al 2002: 2-3). It therefore replaced the NCHS
                                                                          50
1977 charts by CDC 2000 charts that were based on a nation-                                                                 Distribution 4

ally representative sample in which infants came from a                   30
broader spectrum of racial/ethnic groups, socio-economic                  15
backgrounds, and modes of infant feeding.                                2.3
   The discussions surrounding this change led the WHO to deve-             O                                 S                                      Height in inches
lop its own height and weight standards on the basis of a more          Key: (i) Distribution 1: Actual height distribution of five-year-old boys in a given year
                                                                        (ii) Distribution 2: Height distribution of the same five-year-old boys achievable in the
diverse reference population. It collected data on 8,440 healthy        same year with balanced diet for all (iii) Distribution 3: The best height distribution
breastfed infants and young children from Brazil, Ghana, India,         achievable for five-year-old boys of a future generations of these children
                                                                        (iv) Distribution 4: Height distribution of five-year-old boys in the reference population.
Norway, Oman, and the US and adopted new standards in
2006. Almost all developing countries, including India, now                Figure 10 illustrates the above points with the help of four
use these WHO 2006 standards to measure malnutrition. The               strictly hypothetical population distributions of five-year-old
estimate that approximately half of Indian children are mal-            boys. On the horizontal axis, we measure height in inches. On
nourished is based on an application of these standards.                the vertical axis, we measure cumulative population below the
                                                                        height shown on the horizontal axis. The curve labelled “Dis-
6 The Key Elements of the Critique                                      tribution 4” represents the tallest population anywhere in the
Central to the present critique is a challenge to the assumption        world and serves as the reference population. Since 2.3% of
that the provision of a fully balanced diet will eliminate the          this population is below the height labelled S, the height at
height and weight differences between the population of Indian          point S serves as the norm.
children and the healthiest existing population of children                “Distribution 1” gives the observed height distribution of a
any where, which is currently represented by the WHO refer-             hypothetical population of five-year-old boys in a given year.
ence population. Potentially, the failure can occur at two levels.      As shown, 50% of these boys have heights below point S and
   First, we have what has been called the “gradual catch-up”           are therefore classified as malnourished. “Distribution 2”
hypothesis whereby it takes several generations of balanced             shows the height distribution that the population can achieve
diet for a population of children to achieve its full potential         if every boy in it is given a fully balanced diet. It shows that
height and weight.8 Put differently, even if a fully balanced           even after every boy is given a balanced diet, we would clas-
diet replaces the status quo diet of an entire cohort of mal-           sify 30% of them as stunted. Finally, “Distribution 3” shows the
nourished children, it can achieve only so much improvement             “maximum-height” distribution that the future generations of
in height and weight outcomes.9 Weak and malnourished                   this population can achieve after eliminating entirely the
mothers give birth to children with height and weight disad-            “catch-up” deficit through a sustained balanced diet. If the
vantages that a balanced diet cannot fully eliminate. Prema-            genetic potential of this population is below that of the refer-
ture births and lack of proper care during pregnancy give rise          ence population, “Distribution 3” will lie above “Distribution
to similar problems. Therefore, what we may call the “catch-            4”, and if not, it will coincide with or lie below the latter. As
up” deficit takes several generations to eliminate.                     shown, “Distribution 3” is strictly above the reference popu-
   Second, there is the possibility that a specific population of       lation distribution with 15% of the boys still classified as
children is genetically shorter than the children in the refer-         malnourished.
ence population. This means that even after the population                 There is broad agreement that cumulative height “Distribu-
has fully eliminated the “catch-up” deficit after several genera-       tion 2” in India lies above “Distribution 4”. That is to say, even
tions of a balanced diet, it will still fall short of reproducing the   if the entire current population of children were given a bal-
reference population. An example, discussed at length later in          anced diet, it still would not achieve the height distribution of
the paper, is that Japanese adults have grown much taller               the reference population. But almost all analysts explicitly or
on average over the generations. They have, nevertheless,               implicitly see nothing wrong with the current approach under
remained 12 to 13 centimetres shorter than their Dutch coun-            which “Distribution 2” would lead us to classify 30% of India’s
terparts. Poor nutrition and the “catch-up” deficit cannot ex-          children as malnourished.10 At least from the viewpoint of policy
plain this height difference between Japanese and Dutch                 formulation, we need to make a distinction between the 20%
adults unless one argues that the Japanese are still in the             in this example who can cross the threshold after being given a
“catch-up” process.                                                     balanced diet and the remaining 30% who are classified as
Economic & Political Weekly   EPW   may 4, 2013   vol xlviiI no 18                                                                                                103
SPECIAL ARTICLE

malnourished despite receiving such a diet. Without such a           with a separate room used as kitchen and whose family owns a
distinction, we would run the risk of biasing policy towards         car, colour television, telephone, and refrigerator”. This nar-
obesity for this 30% of the population.                              rowing down shrinks the sample to a mere 212 elite or privi-
   The dominant view around the world is that “Distribution 3”       leged children in India. Continuing to apply WHO 2006 growth
coincides with “Distribution 4”. That is to say, a balanced diet     charts, even in this group, 20% children remain stunted and
over several generations will lead to height distribution of         9.4% remain underweight.
every population of children becoming coincident with the               A follow-up report by the Government of India (2009) anal-
distribution of the tallest population in the world. In this view    yses the data from NFHS-3 using an even stricter definition of
there are no genetic differences between populations as far as       elite children. It defines them as children “whose mothers and
height and weight are concerned.                                     fathers have secondary or higher education, who live in house-
   I will document below substantial evidence from medical           holds with electricity, a refrigerator, a TV, and an automobile
and other literature pointing to genetic differences across          or truck, who did not have diarrhoea or a cough or fever in the
populations. But as a preliminary point, it may be noted that        two weeks preceding the survey, who were exclusively breast-
when recommending the switch from NCHS 1977 standards to             fed if they were less than five months old, and who received
CDC 2000 standards, even the CDC cited limited genetic, geo-         complementary foods if they were at least five months old”
graphic, cultural, and socio-economic variability in the former      (GOI 2009: 10). Applying WHO 2006 standards, the report esti-
sample as a key reason for its recommendation (Kuczmarski et al      mates the proportion of stunted children among these elite
2002: 2-3). The argument rationalising the shift, thus, in essence   children to be approximately 15%.
acknowledged the relevance of genetic factors. Against this             Prima facie, these findings imply that even if the popula-
background, we must also ask whether the WHO 2006 sample,            tions of children underlying the NFHS-2 and NFHS-3 were pro-
collected from countries as diverse as Brazil, Ghana, India,         vided a balanced diet and other amenities that lead to good
Norway, Oman, and the US, adequately represents the popula-          height and weight outcomes, 15% to 20% of them will remain
tion of India or any other country in terms of their genetic,        stunted by the WHO 2006 standards. It can be further argued
geographical, cultural, and socio-economic backgrounds.              that even these percentages understate the extent of stunting
                                                                     despite a healthy diet due to two possible selection problems.
7 Evidence from Indian Data                                          First, given that wealth persists over generations, the elite
The two standards that the WHO has recommended over the              children identified in Tarozzi (2008) and GOI (2009) are prob-
decades – NCHS 1977 and WHO 2006 – lead to substantially dif-        ably farther along the “catch-up” curve than the rest of the
ferent levels of measured malnutrition. Using the sample of          population. Therefore, even if the non-elite children were
children under 3 years of age in the second round of the             given the same diet and other amenities, they would exhibit a
National Family Health Survey (NFHS-2), Tarozzi (2008) esti-         higher incidence of stunting and underweight than their elite
mates that the NCHS 1977 standard leads to classifying 42% of        counterparts. Second, it is also possible that genetically taller
these children as stunted. But when the WHO 2006 standard is         children are represented in disproportionately large numbers
applied to the same sample, the estimate rises to 48%. One can       in the populations of elite children analysed by Tarozzi (2008)
imagine that over time populations in the same countries             and GOI (2009). This may result, for example, from genetically
from which the WHO has drawn its sample will become                  taller individuals achieving success in disproportionately
healthier, yielding an even higher standard, which would turn        larger numbers during the earlier part of India’s development
yet more children in the NFHS-2 sample from well-nourished           process and holding on to their lead.
to malnourished.                                                        These findings and arguments show that the absence of a
   Indeed, the problem turns out to be far deeper than what          balanced diet alone cannot fully explain the estimates of
these remarks suggest. NFHS-2 data divide the families in the        stunted and underweight children in India. The “gradual
sample into three wealth categories – high, medium and low –         catch-up” hypothesis or genetic differences or both are at work
on the basis of a standard of living index (SLI) constructed         as well. Deaton and Dreze (2009), who carefully review the
from ownership of a large number of assets and other wealth          findings of Tarozzi (2008), reach the same conclusion. They
indicators. With the help of this index, Tarozzi isolates families   discuss the possibility of genetic differences but favour the
in the high wealth category. This brings down the number of          “gradual catch-up” hypothesis as the explanation for the high
children in the sample from tens of thousands to approxi-            proportions of stunted and underweight children even among
mately 5,100. Measuring against WHO 2006 growth charts,              the elite. They state, “The genetic potential hypothesis, al-
Tarozzi (2008: Table 4, last row) finds that both among boys         though certainly not disproved, is becoming less accepted in
and girls in this group, approximately one-third remain              the scientific literature, if only because there is a long history
stunted and one-quarter underweight.                                 of differences in population heights that were presumed to be
   Tarozzi (2008: 463) explores the issue further by “using          genetic, and that vanished in the face of improved nutrition.”11
only information from families where malnutrition should be             I will argue that while the “gradual catch-up” hypothesis is
unlikely”. Out of the 5,100 children in high SLI families, he        definitely at work in India, it is insufficient to explain the
selects those “from urban areas, where both parents have at          differences in the incidence of child malnutrition between
least a high school diploma, live in a house with a flush toilet     India and Sub-Saharan Africa. Genetic differences remain a
104                                                                             may 4, 2013   vol xlviiI no 18   EPW   Economic & Political Weekly
SPECIAL ARTICLE

necessary part of the full explanation of these differences. I                   This evidence aside, even the references cited by Deaton
reinforce this conclusion by providing evidence of genetic dif-               and Dreze (2009) – Cole (2003) and Nube (2008) – do not sup-
ferences across populations around the world.                                 Table 3: Height Differences across High-income              port the proposi-
                                                                              Countries (cm)                                              tion that the dif-
8 Height Differences Have Not Vanished: Adults                                Country                Male Female          Age        Year
                                                                                                                                          ferences between
                                                                              Netherlands          183.2 169.9 20-30               2010
I noted above that the justification Deaton and Dreze (2009)                                                                              heights of different
                                                                              Sweden               181.5 166.8 20-29              2008
provide for the rejection of genetic differences across popula-                                                                           populations vanish
                                                                              Germany                181       168 18-25          2009
tions is that “there is a long history of differences in population                                                                       with improved nu-
                                                                              US                   177.6 163.2 20-29 2003-06
heights that were presumed to be genetic, and that vanished in                United Kingdom 177.1 164.4 16-24                     2010
                                                                                                                                          trition. Cole (2003:
the face of improved nutrition” (emphasis added). In providing                Canada                 176 163.3 25-44              2005 162) documents the
this justification, the authors do not specify if they have in                South Korea          173.7 161.1 17-18               2011 steady increases in
mind here the differences in adult or child heights or heights at             Portugal             173.7 163.7             21      2001 height over genera-
all ages. But since they refer to the contributions by Cole                   Japan                170.7       158         17      2011 tions in countries
(2003) and Nube (2008) in this context and these authors con-                 Singapore            170.6       160 17-25           2003 such as the US, the
                                                                              Source: Excerpted from the table in http://en.wikipedia.
sider both adult and child nutrition, it is appropriate to con-               org/wiki/Human_height (accessed 11 October 2012),
                                                                                                                                          Netherlands, and
sider both populations.                                                       which also gives the original sources of the data.          Japan but makes no
   Begin with the evidence on whether improved nutrition                                                                                  claims that these
over several generations causes the differences in adult                      differences eventually vanish. Indeed, he explicitly notes,
heights to vanish. In his lively essay entitled “The Height Gap”              “Height in the USA, the most affluent nation, currently lags
in the New Yorker, Burkhard Bilger (2004) traces the fascinat-                behind that in Northern Europe”. He goes on to state, “These
ing history of the literature on the subject. Going by his                    differences are substantial”.
account, evidence supporting the hypothesis of improved                          Nube (2008) does not analyse the height dimension of nutri-
nutrition leading to the elimination of height difference remains             tion and instead focuses on body mass index (BMI). He specifi-
the Holy Grail of researchers in this field. Bilger reports that              cally focuses on south Asians living in various parts of the
US soldiers were two inches taller than the average German                    world and reaches the conclusion that genetic factors are par-
during the first world war.                                                   tially responsible for the low BMI among them. It is instructive
   But sometime around 1955 the situation began to reverse [with Ger-         to quote a key paragraph from his paper in its entirety.
   mans surpassing the Americans in height]. The Germans and other               Results from countries that are home to sizeable population segments
   Europeans went on to grow an extra two centimetres a decade, and              from different ethnic backgrounds, including people of Asian and Af-
   some Asian populations several times more, yet Americans haven’t              rican descent, reveal consistently higher prevalence rates of low BMI
   grown taller in 50 years. By now, even the Japanese – once the shortest       among people of South Asian descent. These differences cannot be
   industrialised people on earth – have nearly caught up with us [Ameri-        explained on the basis of indicators which relate to access to food, so-
   cans], and Northern Europeans are three inches taller and rising              cial status of women or overall standard of living. Apart from the pre-
   (2004: 7; emphasis added).                                                    sented results on South Africa, Fiji and the USA, similar results are also
  John Komlos, a professor of economics at the University of                     reported for England, although in these reports information on the
                                                                                 socio-economic status of the various ethnic population segments is not
Michigan and a pioneer in the field, has thoroughly analysed
                                                                                 presented. On the basis of these outcomes it is hypothesised that there
the data for signs of catch-up by US adults but found none. In                   exists among adults of South Asian descent an ethnically determined pre-
the words of Bilger,                                                             disposition for low adult BMI. This ethnic predisposition can be based
                                                                                 on both genetic and cultural factors (2008: 512; emphasis added).
   But recently he [Komlos] has scoured his data for people who’ve
   bucked the national trend. He has subdivided the country’s heights by         Interestingly, in an earlier paper, Deaton (2007) himself
   race, sex, income, and education. He has looked at whites alone, at        analyses height data from 43 developing countries and finds
   blacks alone, at people with advanced degrees and those in the highest     that no variables, including those relating to nutrition as meas-
   income bracket. Somewhere in the United States, he thinks, there           ured by calorie intake, explain the differences across coun-
   must be a group that’s both so privileged and so socially insulated that
                                                                              tries. He finds the high stature of Africans the hardest to ex-
   it’s growing taller. He has yet to find one (2004: 10).
                                                                              plain, admitting, “Perhaps the major puzzle is why Africans
   Adult height differences magnify as we expand the compari-                 are so tall” (ibid: 132-35). Variables such as per capita income
son to a larger group of high-income countries. Table 3 reports               in childhood, incidence of infant and child mortality rates, per
comparable heights of men and women in several of these                       capita calorie availability, and mother’s education, conven-
countries with the countries arranged in declining order of                   tionally considered to correlate with height, all fail to explain
height of men. Male height in the Netherlands is shown to be                  the exceptionally tall stature of African men and women.
12.5 cm greater than in Japan. Even the gap between male                         Unable to resolve the puzzle, Deaton goes so far as to state,
heights in the Netherlands and Portugal is 9.5 cm. Similar dif-                  Given that Africans are deprived in almost all dimensions, yet are taller
                                                                                 than less-deprived people elsewhere (although not more than Europeans
ferences exist in female heights. In broad terms, both men and
                                                                                 or Americans), it is difficult not to speculate about the importance of
women in northern Europe are the tallest and those in Asia the                   possible genetic differences in population heights. Africans are tall de-
shortest. An interesting observation is that South Korea has                     spite all of the factors that are supposed to explain height (2007:132-36).
overtaken both Japan and Singapore even though its per capita                   But he stops short of accepting the genetic factor as an
income is still far below that of the latter.                                 explanation, arguing that it does not explain the differences
Economic & Political Weekly   EPW   may 4, 2013   vol xlviiI no 18                                                                                       105
SPECIAL ARTICLE

between other populations. In effect, he leaves unexplained                   are at least two problems with this argument. First, as I
the tall stature of Africans despite greater deprivation relative             have already documented, as a matter of general proposition,
to other countries.                                                           adult differences in heights persist across races and ethni-
                                                                              cities. Second, the pattern found for AIA children by Alexander
9 Height Differences between Child Populations                                et al (2007) has also been observed for Japanese-American
It is puzzling that despite having discussed at length the inex-              children.
plicably tall stature of African adults relative to those from                   In particular, Mor, Alexander, Kogan, Kieffer and Ichiho
other poor countries in Deaton (2007), Deaton and Dreze                       (1995) compare the birth outcomes of US-resident white and
(2009) makes no attempt to draw out its implications for the                  Japanese-American mothers using 1979-90 linked live birth
puzzle of lower incidence of stunting among children in nearly                and infant death records from the state of Hawaii. The major-
all Sub-Saharan African countries than in India. One imagines                 ity of these Japanese-American mothers were born in Hawaii
that the two puzzles are intimately linked. But nowhere in the                and the majority of the white mothers were born in the main-
paper do the authors mention this possibility.                                land US. Summarising their findings, the authors state,
   A possible explanation of this oversight may be the belief                      After controlling for maternal socio-demographic and prenatal care
that differences in heights for reasons other than nutrition do                    factors with logistic regression, Japanese-American infants had sig-
not appear in childhood. But evidence fails to support this                        nificantly higher risks of low birth weight, preterm and very preterm
proposition as well. Height and weight differences can be                          birth and of being small-for-gestational age.
found even between populations of newborns who are other-                        It is difficult to attribute these differences to a “catch-up”
wise perfectly equally healthy.                                               deficit among the Japanese-American mothers, especially
                                                                              since the infant mortality rates for the Japanese children, like
9.1 Height and Weight Differences between Equally                             those for the American children, were reported by the authors
Healthy Populations of Newborns                                               to be below the US Year 2000 Health Objective.
In a paper entitled “Birth Outcomes of Asian-Indian-Ameri-
cans”, Alexander, Wingate, Mor and Boulet (2007) compare                      9.2 Older Children and Persistent Height Differences
infants born in the US to resident Asian-Indian-American                      Systematic studies of older children of migrant populations
(AIA) mothers to those born to resident non-Hispanic white                    settled in the developed countries provide additional evidence
and non-Hispanic African-American (AA) mothers. The sam-                      of persistent differences across populations despite improved
ple includes more than 1,00,000 AIA children, more than                       nutrition over some generations. Fredriks et al (2004) col-
three million white children, and more than one million AA                    lected cross-sectional growth and demographic data on 2,880
children. The authors are also able to control for the relevant               children of Moroccan origin and 14,500 children of Dutch ori-
maternal characteristics. They summarise their key findings                   gin living in the Netherlands in the age range 0 to 20 years in
as follows.                                                                   1997. Their findings are consistent with our previous discus-
  Compared to AAs or Whites, AIAs have the lowest percentage of births to     sion. “Moroccan young adults were on average 9 cm shorter
  teen or unmarried mothers and mothers with high parity for age or with      than their Dutch contemporaries. …Height differences in com-
  low educational attainment. After taking these factors into account, AIA    parison with Dutch children increase from 2 years onwards.”
  had the highest risk of LBW [low birth weight], small-for-gestational age
                                                                              These authors find the differences so compelling that they rec-
  (SGA) and term SGA births but a risk of infant death only slightly higher
  than Whites and far less than AAs. Conclusions: The birth outcomes of
                                                                              ommend drawing up separate growth charts for Moroccan
  AIAs do not follow the paradigm that more impoverished minority popu-       and Dutch children.
  lations should have greater proportions of low birth weight and preterm        Indeed, today, it is possible to find separate growth charts
  births and accordingly greater infant mortality rates.                      for children of Moroccan and Dutch origin living in the Neth-
   The authors speculate that the small body size and low birth               erlands, making it possible to compare the two populations.12
weight of AIA children may be due to either “certain genetic                  Table 4 reports mean heights in centimetres in 2010 for these
factors related to the shortness or smaller size of the mother                two populations. Differences are minimal at the first year but
caused by undernourishment occurring during childhood” or                     positive and rising from the second years onwards. By the
“a different body habitus among this ethnic group and maybe                   third year, the difference is a full centimetre and grows to 1.8
due to genetic factors, not suboptimal growth”. Whatever the                  cm for boys and 2.7 cm for girls in the fifth year. By the fifth
reason, the authors’ findings are that the AIA children are fully             Table 4: Height of Moroccan and Dutch Children in the Netherlands
caught up with white and AA children of similar socio-eco-                    in 2010 (in cm)
nomic and demographic backgrounds in terms of infant mor-                     Age in Years              Boys                                         Girls
                                                                                             Moroccan   Dutch        Difference    Moroccan         Dutch    Difference
tality but continue to exhibit higher incidence of low birth
                                                                              1               76.1       76.7           0.6           75             75           0
weight and small size for gestational age.
                                                                              2               87.7       88.4           0.7         86.5            87.1        0.6
   Even so, it is tempting to invoke the “gradual catch-up” hy-
                                                                              3               96.8       97.8             1          96              97           1
pothesis and argue that over several generations, Indian
                                                                              4              104.5      105.5             1        103.5           104.9        1.4
mothers will catch up with the American mothers in height                     5              111.4      113.2           1.8        110.2           112.9        2.7
and weight, thus bridging the size and weight gap between                     21             177.8      183.8             6        162.8           170.7        7.9
the two sets of children that is currently observed. But there                Source: www.tno.nl

106                                                                                             may 4, 2013     vol xlviiI no 18     EPW      Economic & Political Weekly
SPECIAL ARTICLE

year, the gaps are thus almost a third of the gaps obtained at                wrong for a number of reasons, some of which Tarozzi is him-
full adulthood – 6 cm for boys and 7.9 cm for girls.                          self careful to note.
   Fredriks et al (2004) are not alone in finding persistent                     For starters, observe the qualification “prima facie” in the
differences between populations of children of migrants in de-                statement. Tarozzi is tentative and by no means conclusive in his
veloped countries and those of local families. Smith et al                    tone. And there are good reasons for this caution. The sample
(2003), who compare the heights and weights of 6-12 year old                  with which he works is extremely inadequate to draw strong in-
Maya-American children using samples collected at two dif-                    ferences about the absence of genetic differences. Thus, for ex-
ferent points in time with the National Health and Nutrition                  ample, the sample of children under 2 years of age born to Indian
Examination Survey (NHANES) reference standards for US                        parents in the data set available to him is so small that he does
children, also find the height gap narrowing over time but                    not even attempt a comparison between them and children of
not vanishing.                                                                the same age born to white parents. For children 2 to 3 years old,
   As many as half a million Guatemalan Maya, mostly from                     his sample has just 19 Indian children and for those between 2
rural areas, have migrated to the US since the civil war in Gua-              and 5 years, it has 72 children. Such small samples are quite
temala in 1978. The bulk of this migration took place in the                  inadequate to measure even the average levels of stunted and
1980s. Smith et al compare the heights of Maya children living                underweight children with any degree of precision, let alone the
in the US in 1992 and 2000 with their Guatemalan counter-                     entire distribution of the underlying population.
parts as well as the NHANES standard for American children.                      Moreover, even these small samples do not yield zero differ-
They find that 6-year-old Maya children living in the US in                   ences between stunting levels among children born to Indian
1992 were on an average 6 cm taller than their 1998 Guatema-                  parents and those to local white parents. The proportion of
lan counterparts. They had gained another 3 cm after eight                    Indian children, 2 to 3 years old, who are placed in the stunted
years in 2000. Nevertheless, they remained 5 cm shorter than                  category by the WHO 2006 definition is 5.3% compared with
the NHANES standard for American children in 2000.                            nil among white children. Surely, the difference between 5.3%
   A defender of uniform worldwide norms for measuring nu-                    and 0% is not zero. Moreover, if we were to make the height
trition may argue that the reason Moroccan children in the                    norm against which stunting is evaluated even more demand-
Netherlands and Maya children in the US lag behind their host                 ing than the WHO 2006 norm, the proportion of Indian chil-
country counterparts in height is that they still have not had                dren who are stunted would rise, whereas it may still remain
enough time to eliminate the “catch-up” deficit. It is possible that          zero among white children.13
the remaining gap will be eliminated in another few generations.                 There are more qualifications to the conclusion by Tarozzi.
   But this argument has two limitations. First, given that adult             Even if it were true that the height gap between Indian children
height differences across developed country populations have                  born in the UK and their white counterparts is nil, it does not
persisted, as has the incidence of low birth height and low                   prove that at some point in time Indian children born and brought
birth weight between Japanese and American children born in                   up in India will also close the gap. There are at least two reasons
Hawaii, how can we be sure that the height and weight differ-                 for this conclusion. First, there may be a selection problem such
ences between children will vanish in due course? Indeed, the                 that Indian parents who migrated to the UK are disproportion-
weight of evidence remains in favour of the differences nar-                  ately drawn from a part of the population that is taller for genetic
rowing but not vanishing. Second, as previously stated, from a                reasons. Those who chose to migrate may have on average en-
policy standpoint, what sense does it make to attribute dif-                  joyed some genetic advantage over the population left behind.
ferences in height and weight that can only be bridged over                      Tarozzi himself is aware of this possibility and is careful to
future generations to malnourishment?                                         highlight it. Immediately following the conclusion quoted
                                                                              above, he states,
9.3 Children of Indian Migrants in the UK                                       Of course, these findings are not sufficient to disprove the claim that
At least some analysts who believe that height differences                      genetic factors play a role in explaining the relative disadvantage in
across populations of children can be eliminated by good nutri-                 growth pattern of children, such as those sampled within the NFHS,
                                                                                who are born and raised in India. To argue that ethnic Indians who
tion have relied on a comparison of children born to Indian
                                                                                live in the UK share the same genetic characteristics in terms of growth
(and Pakistani and Bangladeshi) parents settled in the UK with                  potential as their counterparts still living in India, one should argue
those born to white parents in the study by Tarozzi (2008).                     that migration to the UK is uncorrelated with growth potential. How-
This necessitates a close examination of his following finding.                 ever, there are reasons to suspect that correlation may exist, as mi-
                                                                                grants are often taller (2008: 464).
   Overall, these results [shown in his Table 6] provide some prima facie
   evidence in support of the hypothesis that the growth performance of          Second, even assuming that migrant parents are representa-
   children of Indian ethnicity who live in the UK is comparable to that of   tive of the Indian population, the possibility that the gap will
   the reference population used to construct either the WHO-2006 or the      persist in the case of children born and raised within India
   CDC-2000 references (2008: 464; emphasis in the original).
                                                                              cannot be ruled out. What if the UK geography, culture, and
  A casual reader already unsympathetic to the possibility of                 environment are more conducive to height and weight deve-
genetic differences is likely to conclude from this statement                 lopment of children than the Indian geography, culture, and
that the key assumption underlying the WHO-sanctioned                         environment? Therefore, what is needed is evidence that some
methodology to measure malnutrition is valid. Yet, she will be                sub-populations of children born and raised within India have
Economic & Political Weekly   EPW   may 4, 2013   vol xlviiI no 18                                                                                 107
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