• TABLE OF CONTENTS
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 Front Cover
 Title Page
 List of contents and tables
 Foreword
 Acknowledgement
 Recent literature on the interactions...
 New evidence on the synergism between...
 Implications for future research...
 Postscript: Is there a definitional...
 Appendix
 Reference
 Back Cover
 Reprint permission notice














Group Title: Food, agriculture, and the environment discussion paper - International Food Policy Research Institute; no. 16
Title: Managing interactions between household food security and preschooler health
CITATION THUMBNAILS PAGE IMAGE ZOOMABLE
Full Citation
STANDARD VIEW MARC VIEW
Permanent Link: http://ufdc.ufl.edu/UF00085351/00001
 Material Information
Title: Managing interactions between household food security and preschooler health
Series Title: Food, agriculture, and the environment discussion paper
Physical Description: vi, 22 p. : ; 28 cm.
Language: English
Creator: Haddad, Lawrence James
International Food Policy Research Institute
Publisher: International Food Policy Research Institute
Place of Publication: Washington DC
Publication Date: 1996
 Subjects
Subject: Malnutrition in children   ( lcsh )
Food supply   ( lcsh )
Preschool children -- Nutrition   ( lcsh )
Genre: bibliography   ( marcgt )
non-fiction   ( marcgt )
 Notes
Bibliography: Includes bibliographical references (p. 20-22).
Statement of Responsibility: Lawrence Haddad ... et al..
General Note: "June 1996."
General Note: "2020 vision"--Cover.
 Record Information
Bibliographic ID: UF00085351
Volume ID: VID00001
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Rights Management: All rights reserved by the source institution and holding location.
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Table of Contents
    Front Cover
        Front Cover 1
        Front Cover 2
    Title Page
        Page i
        Page ii
    List of contents and tables
        Page iii
        Page iv
    Foreword
        Page v
    Acknowledgement
        Page vi
    Recent literature on the interactions of diet and infections
        Page 1
        Page 2
    New evidence on the synergism between household food insecurity and morbidity for preschooler nutrition
        Page 3
        Page 4
        Page 5
        Page 6
        Page 7
    Implications for future research and programs
        Page 8
        Page 9
    Postscript: Is there a definitional continuum between food security and nutrition, and is it useful?
        Page 10
    Appendix
        Page 11
        Page 12
        Page 13
        Page 14
        Page 15
        Page 16
        Page 17
        Page 18
        Page 19
    Reference
        Page 20
        Page 21
        Page 22
    Back Cover
        Page 23
        Page 24
    Reprint permission notice
        Page 25
Full Text











Managing Interactions
between Household
Food Security and
Preschooler Health
Lawrence Haddad
Saroj Bhattarai
Maarten Immink
Shubh Kumar








202o
VISION








































"A 2020 Vision for Food, Agriculture, and the En\ ironment" is an initiative of
the International Food Policy Research Institute (IFPRI) to de\ elop a shared
vision and a consensus for action on how to meet future world food needs
while reducing poverty and protecting the environment. It grew out of a
concern that the international community is setting priorities for addressing
these problems based on incomplete information. Through the 2020 Vision
initiative, IFPRI is bringing together divergent schools of thought on these
issues, generating research, and identifying recommendations.

This discussion paper series presents technical research results that encom-
pass a wide range of subjects drawn from research on policy-relevant
aspects of agriculture, poverty, nutrition, and the environment. The discus-
sion papers contain material that IFPRI believes is of ke\ interest to those
involved in addressing emerging Third World food and development prob-
lems. These discussion papers undergo review but typically do not present
final research results and should be considered as works in progress.















Managing Interactions between

Household Food Security

and Preschooler Health


Lawrence Haddad
Saroj Bhattarai
Maarten Immink
Shubh Kumar










International Food Policy Research Institute
1200 Seventeenth Street, N.W.
Washington, D.C. 20036-3006 U.S.A.
June 1996



















Contents


Foreword v
Acknowledgments vi
Recent Literature on the Interaction of Diet and Infection 1
New Evidence on the Synergism between Household Food Insecurity and
Morbidity for Preschooler Nutrition 3
Implications for Future Research and Programs 8
Postscript: Is There a Definitional Continuum between Food Security and Nutrition,
and Is It Useful? 10
Appendix 11
References 20




Tables


1. Means of selected indicators for preschoolers in eight countries 4
2. Probability of not being weight deficient among preschoolers in Ethiopia,
Pakistan, and the Philippines at different levels of calories per capital and
selected cutoffs of predicted probability of diarrhea 6
3. Summary of IFPRI work on determinants of anthropometric status 11
4. Brief description of data sets 12
5. Regression summary of preschoolers, Ethiopia 14
6. Regression summary of preschoolers, Pakistan 16
7. Regression summary of preschoolers, the Philippines 18




















Foreword



Food security does not assure good nutrition. The nutritional status of an individual is
influenced not only by food but also by nonfood factors, such as clean water, sanitation, and
health care. The effect of all of these factors must be considered in efforts to rid the world of
malnutrition. Food security will result in good nutrition only if nonfood factors are effectively
dealt with. In this paper, Lawrence Haddad, Saroj Bhattarai, Maarten Immink, and Shubh
Kumar show how malnutrition among preschool children is determined by a complex interac-
tion of illness and lack of food.
The authors look at three countries-Ethiopia, Pakistan, and the Philippines-to study
how food availability and diarrhea interact and what this interaction means for preschooler
malnutrition. Their results show that the links between food consumption, diarrhea, and
malnutrition are stronger than most economic studies have assumed. When diarrhea is preva-
lent, the effects of food shortages on child malnutrition are worse, and when food is scarce, the
effects of diarrhea on child malnutrition are worse.
These findings have important implications for policy and programs. If factors other than
food are critical elements in turning food consumption into good nutrition, then policymakers
will need to ensure that the poor do not have to choose between, for example, food and health
care.
This paper is a product of IFPRI's 2020 Vision initiative, which seeks to develop an
international consensus on how to meet future world food needs while reducing poverty and
protecting the environment.

Per Pinstrup-Andersen
Director General, IFPRI














Acknowledgments


The authors thank Harold Alderman, Lynn Brown, Patrice Engle, Per Pinstrup-Andersen, and
Marie Ruel for comments. Any remaining errors are the responsibility of the authors.





















current theory holds that good nutrition for
preschoolers depends on household food
security, an adequate health environment, and
adequate maternal and child care (UN ACC/SCN
1991).' It is increasingly clear, however, that
nutrition status is a product not only of the levels of
these three factors, but also of the interactions
between them. For example, von Braun and Ken-
nedy find little evidence to support the hypothesis
that the improved income and food availability from
agricultural commercialization translate into im-
proved child nutrition.2 They state,

Increased income and increased food
availability contribute to solving the hunger
problem but not the problem of preschool
children's malnutrition, which results from
a complex interaction of lack of food and
morbidity (von Braun and Kennedy 1994,
374-375).

To date, there has been little empirical research
examining the magnitude and policy relevance of
the interactions between food insecurity and mor-
bidity--especially in a multicountry comparison.
This paper argues that (1) a better understanding of
the magnitude of the interactions will lead to more
cost-effective policies and programs for the im-
provement of child nutrition today, and (2) as 2020
approaches, the need to appreciate these interactions
will become even greater.


First, a brief review of the literature reveals that
nutritionists have carefully studied these interac-
tions, but economists have not. Second, the paper
uses household data from eight countries to examine
the relative importance of household food security
and self-reported diarrhea prevalence and interac-
tions between them for the production of child nutri-
tion. Third, it traces the implications of these results
for research and policy in light of some current
global trends. Finally, it discusses a wider concept
of food security that is consistent with the results
here and that could lead to improved research and
programming for the good nutrition of children.


Recent Literature on the
Interaction of Diet and Infection
This brief literature review is intended to be repre-
sentative of the wider literature on the interactions
between food and nonfood inputs into nutrition, but
is by no means a comprehensive treatment of it. The
literature review is divided along disciplinary lines
to give a flavor of the different debates within the
nutrition and economics communities.

The Nutrition Literature
It is well-known that current diet and nutrient avail-
ability influence the body's immune response
capacity in later time periods. This body of work


1The generally accepted definition of household food security says that "a household is food secure when it has access to the food needed for a healthy
life for all its members (adequate in terms of quality, quantity, safety, and culturally acceptable), and when it is not at undue risk of losing such access"
(UN ACC/SCN 1991, 6).
2A case from the Philippines provides an interesting exception. Here, the adoption of sugarcane production reduced the demand for female labor in
heavy activities, led to improved maternal nutrition, allowed increased child care time, and improved anthropometry for children under age one. As
these preschoolers got older, however, their anthropometric status slipped back to match that of other preschoolers as a result of a poor community
health and sanitation infrastructure (Bouis and Haddad 1990).











was summarized by R. K. Chandra in his 1990
McCollum Award Lecture (Chandra 1991). This
literature shows that general protein energy malnu-
trition-as measured by child growth-and specific
nutrient deficiencies diminish the body's ability to
resist infection, leading to a higher incidence, longer
duration, and greater severity of illness. Malnutri-
tion induced by dietary and related factors contrib-
utes to the breakdown of a wide range of human
defense mechanisms that protect against infections.
Mechanisms include physical barriers (skin and
mucous membranes), nonspecific mechanisms (in-
terferon, lysozyme, and phagocytes), and antigen-
specific processes (antibodies and cell-mediated
immunity).
This literature suggests that an initial nutritional
deficiency may contribute to an increased risk of
infections, which leads to a downward spiral of
malnutrition (increased loss, reduced intake) and
infection. Studies have consistently shown that pri-
mary or secondary malnutrition and cell-mediated
immune deficiency are important independent risk
factors in the prevalence of common diseases, such
as diarrhea. Relatively few studies have measured
immunocompetence with nutrient interventions in
order to assess the time frame for the buildup and
reduction in human immunocompetence. One re-
cent example, involving beta-carotene administra-
tion, found that it took nine months of dietary
supplementation in healthy adults for a significant
increase in immunocompetence against viral
infections to occur (Murata et al. 1994). Some re-
cent zinc supplementation trials on young preschool
zinc-deficient children have, however, shown a
quicker response in diarrhea reduction as a result of
supplementation (Sazawal et al., forthcoming).
Just as diet affects infection, infection affects
food intake and the utilization of foods. Infection
(1) increases nutrient requirements because of in-
creased nutrient loss during illness, (2) reduces die-
tary intake through appetite suppression, and
(3) creates a metabolic response that both stimulates
the immune response and suppresses body growth.
On the first point, for instance, during the acute-


phase response to infection, the metabolism of
many nutrients is altered and the requirements for
some nutrients are increased (Beisel 1984). During
acute respiratory infections, for example, vitamin A
requirements increase substantially because of the
excretion of retinol in the urine (Stephensen et al.
1994). Measles and diarrhea have also been associ-
ated with an increased vitamin A deficiency
(Bhaskaram et al. 1984) that contributes to the de-
pletion of body stores (Campos, Flores, and Under-
wood 1987) and the emergence of severe deficiency
symptoms such as xerophthalmia (Mahalanabis
1991).

The Economics Literature
Economists tend to represent the production of nu-
trition as analogous to the production of any other
commodity, in that inputs are transformed, at a
given level of technology, into output. The major
inputs into child nutrition are food intake, health
levels, and care3 (UN ACC/SCN 1991; Strauss and
Thomas 1994).
The economics literature has not, however,
taken as balanced a view as the previous sentence
implies. First, a relatively large amount of time has
been spent on the measurement of food consump-
tion and relatively little on the validity, reliability,
and usefulness of measures of morbidity that rely on
self-reported information (Strauss and Thomas
1994). Second, food intake is more likely to be
handled properly from an econometric viewpoint
(that is, treated endogenously) than is illness (in that
illness, diet, and weight are, to some extent, simul-
taneously determined). Third, very few studies con-
trol for unobserved heterogeneity (such as the often
unobserved individual and household charac-
teristics of feeding and hygiene caring behaviors).4
These shortcomings aside, most of the studies
find that preschooler diarrhea has a significantly
negative impact on preschooler anthropometry,
controlling for either preschooler or household calo-
rie consumption. Calorie consumption at the house-
hold and preschooler level is positively and signifi-


3Care is defined as "the provision in the household and in the community of time, attention, and support to meet the physical, mental, and social needs
of the growing child and other household members" (ICN 1992, 1). Care is thought to affect nutrition in two broad ways: through dietary intake and
through health and hygiene practices.
4The largest number of similar case studies has probably been generated by IFPRI. Appendix Table 3 summarizes the significant explanatory variables
from Z-score regressions from these studies, together with their limitations.











cantly associated with preschooler anthropometry,
controlling for preschooler diarrhea. Rarely, how-
ever, does one get a sense of the relative magnitude
of the impact of these two inputs on nutrition. More-
over, even in the more sophisticated treatments (for
example, Bhargava 1994), interaction terms are not
used; therefore it is impossible to assess the impact
of disease, such as diarrhea, on preschooler anthro-
pometry at different levels of household calorie con-
sumption. Nor is it possible to assess the impact of
household calorie consumption on preschooler an-
thropometry at different probabilities of diarrhea
episodes.



New Evidence on the Synergism
between Household Food
Insecurity and Morbidity for
Preschooler Nutrition
This section attempts to fill some of the gaps identi-
fied in the previous sections. Specifically, new evi-
dence is presented on the trade-offs between food
security (measured by household food availability)
and preschooler morbidity in the generation of pre-
schooler nutrition as measured by anthropometric
measures. A descriptive analysis of data sets from
eight countries-Ethiopia, Ghana, Guatemala,
Kenya, Nicaragua, Pakistan, the Philippines, and
Zambia-is undertaken to examine the associations
between household food security and access to
health inputs, self-reported morbidity, and nutrition
outcomes.
In addition, a multivariate regression analysis is
undertaken on data from Ethiopia, Pakistan, and the
Philippines. The regression analysis involves the
estimation of a system of three equations explaining
(1) household calorie availability, (2) preschooler
diarrhea outcomes, and (3) the standardized weight-
for-age of preschoolers. In the third regression, spe-
cial emphasis is placed on the relative contributions
of diarrhea and household calorie availability to
improvements in nutrition outcomes. Specifically,


interaction terms are used to explore the impacts of
nutrition inputs at different levels of other inputs.

Descriptive Data from Eight Countries
Table 1 describes a wide range of total expenditure,
food availability, morbidity, and nutrition data from
the eight countries. Table 1 presents the sample of
preschoolers from each country, divided into pre-
schoolers from food-secure and from food-insecure
households.5 The data and the individuals they rep-
resent are briefly described in Appendix Table 4.
In four out of eight data sets-Ghana, Kenya,
Pakistan, and Zambia-preschooler Z-score
weight-for-age is actually worse in households that
are classified as food secure, compared with pre-
schoolers in households classified as food insecure,
although only in Pakistan is the difference statisti-
cally significant. The results for preschooler Z-
score weight-for-height are less striking but show
some similarities. Compared with preschoolers
from households that are food insecure, pre-
schoolers from households that are food secure are
worse off in terms of Z-score weight-for-height in
two out of seven countries-Ghana and Pakistan;
again, only in Pakistan is the result statistically sig-
nificant.6
In terms of self-reported prevalence of diarrhea,
with the exception of Guatemala (where pre-
schoolers from food-secure households have a
diarrhea prevalence rate three times that of food-
insecure households), the prevalence figures are
similar for preschoolers from both types of house-
holds in all the data sets.

Multivariate Analysis for Ethiopia,
Pakistan, and the Philippines
Table 1 is descriptive and may mask confounding
factors. For example, households that are food se-
cure may contain more older preschoolers, and age
tends to be negatively correlated with preschool
anthropometric measures. This section details a
more causal examination of the determinants of Z-
score weight-for-age in Ethiopia, Pakistan, and the


5A household is operationally defined as food secure if calorie intake per adult equivalent is above 2,200, except for Guatemala, where the top three
quartiles of food expenditure per capital is used as a cutoff.
6When the preschoolers in Table 1 are disaggregated into two groups, zero to less than two years old and two to less than five years old, there is no
appreciable strengthening of the relationship between household food security and anthropometric status.














Table 1-Means of selected indicators for preschoolers in eight countries

Bangladesh Ethiopia Ghana

Food- Food- Food- Food- Food- Food-
Insecure Secure Insecure Secure Insecure Secure
Households Households Households Households Households Households
Indicator (n = 635) (n = 940) (n = 439) (n = 808) (n = 123) (n = 141)


Nutrition outcome
Z-scores of weight-for-agea
Z-scores of height-for-agea
Z-scores of weight-for-heighta
Morbidity
Prevalence of diarrhea (1 = yes, 0 = no)
Number of days sick from diarrhea/recall period
Ill including diarrhea (1 = yes, 0 = no)
Total days sick/recall period
Treatment sought 1: 1 = traditional method,
0 = other
Treatment sought 2: 1 = doctor
Treatment sought 3: 1 = health post/health
center/hospital
Treatment sought 4: 1 = co-op clinic
Treatment sought 5: 1 = dispensary/pharmacy/
private clinic
Treatment sought 6: 1 = medical kit from
government
Distance to health services
Water and sanitation
Water source (1 = piped/private well)
Latrine (1 = yes, 0 = no)
Water sufficient ( = yes, 0 = no)
Drainage (1 = septic/sewage line, 0 = other)
Per capital living space (square meters)
Household characteristics
Household calories/adult equivalent unit
Household calories per capital
Household size
Food expenditure per capital
Household expenditure per capitald
Total health expenditure per capital


-2.54* -2.09 -1.1
... -1.1
... -0.59


0.05
0.36
0.13*
1.16*

0.00



0.00


-1.28
-1.19
-0.66


0.09
0.34
0.46
0.85


0.00
0.00*

0.003


0.03* 0.02
... 5.24* 5.63


0.98
0.29*



4.18*


1,640* 2,803


6.41
1,587.1*
2,294.3*
85.37*


6.52
2,853.4
4,008.5
177.40


0.00* 0.06
0.02 0.03


1,752* 3,365
1,382* 2,541
9.08 9.00



1.74* 2.27


0.04
1.00






1,549*
1,197*
9.41*


0.01
1.00






3,567
2,741
7.81


107,808 104,673
2,484.0 2,797.1
















Guatemala Kenya Pakistan The Philippines Zambia

Food- Food- Food- Food- Food- Food- Food- Food- Food- Food-
Insecure Secure Insecure Secure Insecure Secure Insecure Secure Insecure Secure
Households Households Households Households Households Households Households Households Households Households


(n = 47) (n = 160) (n = 484) (n = 654) (n = 531) (n = 1,474) (n = 1,330), (n = 1,559)


(n = 224) (n = 560)


-2.16*
-3.06*
-0.48


0.06*
0.21
0.23*
0.43


-1.75 -1.69
-2.56 -1.55*
-0.23 -0.30


-1.81 -1.39*
-2.07 -2.33*
-0.18 0.49*


0.24
0.95
0.41*
2.14*


0.03
0.09


0.21 0.12
0.04* 0.11


-1.67
-2.20
-0.06


0.21
1.00
0.39
2.70


-1.63*
-2.31*
-0.58


0.03


0.26
1.54


-1.51 -0.63
-2.13 -2.03
-0.55 0.55


0.09*


0.63*


... 0.08



... 0.10


36.15* 59.08 59.92 58.04


0.39*
0.81*


1,629* 3,333 2,065*
1,704*
12.74* 17.09 14.33*
97,618 98,189 ...
10,956.0* 8,826.8 2,148.63*


2,648
2,156
10.28


2,812.94


... 125.23* 161.31


1,759*


7.57*
25.83
37.02*
5.8*


2,799


7.02
32.94
48.89


1,694*
1,189*
9.52*
347.7*
467.9*


6.9 1.20*


Notes: Cutoff used for classifying households as food secure or food insecure is household calories per adult equivalent unit = 2,200. N refers to the
sample of preschoolers. Household variables were merged with each preschooler in the household. The actual number of cases used to compute
means for each variable may vary slightly. In Guatemala, food expenditure quartile was used, and the lowest quartile was the cutoff point at .05
for food-secure and -insecure groups. Leaders (...) indicate not available. indicates significantly different between food insecure and food
secure level.
aThe Z-score is a method used in standardizing the distribution of actual weight and height of the child relative to the standard weight for a child of
that age and height.
bRecall period for morbidity: 1 week for Ghana, Guatemala, and Kenya; 2 weeks for Ethiopia, Pakistan, and the Philippines; 1 month for Zambia; and
3 months for Bangladesh.
CDistance units are all kilometers except in Pakistan and the Philippines, which use minutes. In Guatemala, 1 = < 3 kilometers, 0 = > 3 kilometers.
dExpenditures reported are in local currency of the country per year. In the Philippines it is per week.


-0.71
-2.00
0.72




0.23


1.22

0.13
0.29*

0.17


0.17



0.30


0.62


0.36*
0.22*


0.09*
0.65


15.4


0.35
0.37*


8.47
385.52*


7.92


7.18
801.70


10.45


3,611
2,279
6.07
542.7
771.9
1.76












Table 2-Probability of not being weight deficient among preschoolers in Ethiopia, Pakistan,
and the Philippines at different levels of calories per capital and selected cutoffs of
predicted probability of diarrhea

Predicted Probability of Predicted Probability of Predicted Probability of
Diarrhea for Ethiopiaa Diarrhea for Pakistana Diarrhea for the Philippinesa

Household Calorie High Medium Low High Medium Low High Medium Low
Availability per Capita (0.05) (0.02) (0.006) (0.29) (0.19) (0.11) (0.037) (0.018) (0.009)

750 calories per capital
Without interaction term 0.27 0.30 0.31 0.46 0.48 0.51 0.56 0.58 0.58
With interaction term 0.27 0.37 0.42 0.30 0.45 0.58 0.56 0.62 0.64

1,250 calories per capital
Without interaction term 0.34 0.37 0.38 0.53 0.56 0.59 0.67 0.68 0.69
With interaction term 0.37 0.44 0.49 0.43 0.54 0.63 0.66 0.69 0.71

2,000 calories per capital
Without interaction term 0.44 0.47 0.49 0.64 0.67 0.69 0.80 0.81 0.81
With interaction term 0.54 0.56 0.57 0.63 0.66 0.69 0.79 0.79 0.79
Notes: A preschooler is considered weight deficient if his or her weight-for-age Z-score is <-2. The diarrhea recall period is two weeks for each data
set.
aCutoffs used are the 25th, 50th, and 75th percentile of predicted probability of diarrhea.


Philippines.7 The regression analyses are presented
in detail in Appendix Tables 5 through 7. These
tables lay out the system of equations used to ad-
dress the concerns of endogeneity of household
calorie availability and preschooler diarrhea. Logit
regressions are run for the diarrhea zero-one de-
pendent variable and for the zero-one dependent
variable corresponding to the Z-score weight-for-
age cutoff of-2 standard deviations. Table 2 takes
coefficient estimates from Appendix Tables 5
through 7 to predict the probability of a pre-
schooler's being above an anthropometric cutoff of
-2 standard deviations below the median value of
weight-for-age for a healthy U.S. population (stand-
ards from the U.S. National Center for Health Sta-
tistics are used for all countries). Probabilities are
calculated at different levels of household per capital
calorie availability and at different levels of diar-
rhea probability, and with and without a calories-
diarrhea interaction term.8


For a linear formulation,

Z-score = a + b.calories
+ c.diarrhea probability
+ d.calories*diarrhea probability + ...


the interaction term permits the marginal impact of
calories and diarrhea probability on Z-score weight-
for-age to be interdependent, that is:

az/acalories = b + d.diarrhea probability
and
8z/8diarrhea probability = c + d.calories.


Appendix Tables 5 through 7 show that calories
have a positive and significant impact on Z-score
weight-for-age for all three data sets, and diarrhea


7Weight-for-age is chosen primarily because it is available for all data sets.
'The predicted probability of a preschooler's being above the Z-score weight-for-age cutoff is given by e'/(l + e), where z is a linear combination
of the explanatory variables and the logit regression coefficients in the last two columns of Tables 5 through 7.











prevalence has a negative and significant impact on
Z-score weight-for-age, at least at the 10 percent
level. This result is consistent with a priori expecta-
tions. When included, the interaction term is signifi-
cant and positive for all three countries at the 10
percent level.9
This positive sign on the interaction term indi-
cates that the marginal impact of the extra calorie on
Z-score weight-for-age increases as diarrhea prob-
ability increases. Conversely, the marginal impact
of a loss of the extra calorie on Z-score weight-for-
age also increases as diarrhea probability increases.
High levels of diarrhea magnify the effects of gains
and losses in household food security on child nutri-
tion. The positive sign on the interaction term also
indicates that the negative effect of diarrhea prob-
ability on Z-score weight-for-age is muted at higher
levels of household calorie availability.
However, because of the nature of the dependent
variable (1 if preschooler is above -2 Z-score weight-
for-age, 0 otherwise), logit, not linear, estimation is
used. Reported coefficient estimates from logit esti-
mation do not correspond to marginal impacts but
require a transformation for the recovery of the mar-
ginal impacts. Hence, determining the a priori sign on
the interaction term is not straightforward.
Table 2 therefore takes these estimated logit
coefficients and presents their effects in a more
intuitive manner. Specifically, the estimates are
used to predict the probability of a preschooler's
being above 2 standard deviations of weight-for-
age, calculated based on values of 750, 1,250, and
2,000 for daily per capital household calorie avail-
ability and low, medium, and high predicted prob-
abilities of diarrhea. Two predicted Z-score weight-
for-age probabilities are calculated for each
combination of calorie and diarrhea values: one
with an interaction term in the preschooler Z-score
weight-for-age equation, and one without the inter-
action term. As far as the authors are aware, none of
the previous studies of this sort have used such an
interaction term.
There are several things to observe in Table 2.
First, increased household calorie availability
and decreased diarrhea probability both in-


crease the probability of improved pre-
schooler Z-score weight-for-age-with or
without the interaction term.

Second, for Ethiopia, the inclusion of the in-
teraction term affects the predicted prob-
abilities at all values of calories per capital and
diarrhea probability. In fact, all the predicted
probabilities are higher when an interaction
term is used. For Pakistan, the inclusion of an
interaction term matters only at the two low-
est levels of calorie availability per capital.
For the Philippine data, the inclusion of the
interaction term matters only at the lowest
level of calorie availability per capital' 0
The exclusion of an interaction term
therefore understates the combined impact of
improved household calorie availability and
reduced diarrhea probability on preschooler
Z-score weight-for-height. This suggests that
standard economic analyses of the causes of
child nutrition may be understating the im-
portance of both household food availability
and infant diarrhea in determining whether or
not a preschool child is underweight.

* Third, the inclusion of the interaction term
improves the responsiveness of Z-score
weight-for-age to reductions in diarrhea prob-
ability. For Ethiopia and Pakistan, this holds
for the two lower levels of calorie availability
per capital and, for the Philippines, this holds
only for the bottom calorie availability per
capital level. For example, at 750 calories per
capital for Ethiopia, without the interaction
term the probability of improved Z-score
weight-for-age increases from 0.27 to 0.31 as
diarrhea probability moves from high to low.
With the interaction term, the change in prob-
ability is from 0.27 to 0.42. Thus, increased
diarrhea prevalence at low levels of house-
hold calorie availability per capital has a
greater negative impact on nutrition than does
increased diarrhea prevalence at high levels
of household calorie availability per capital.


9The inclusion of the interaction term does, however, cause the calorie variable to become insignificant for Pakistan and the Philippines, although
the two calorie terms are jointly significant for each data set.
1'These differences may reflect different stages of general development in each of the three study communities.











Fourth, for both Ethiopia and Pakistan, the
sensitivity of preschooler Z-score weight-for-
age with respect to declines in household calo-
rie availability per capital is greater at the
higher diarrhea probabilities. For Pakistan, for
example, the omission of the interaction term
at high levels of morbidity results in a decline
in the predicted probability for improved Z-
score weight-for-age from 0.64 to 0.46 as
household calorie availability per capital de-
clines from 2,000 to 750. With the interaction
term, the probability decline is from 0.63 to
0.30. A similar result holds for Ethiopia. For
the Philippine data, at high levels of morbid-
ity, the inclusion of the interaction term has
little effect on predicted Z-score probabilities
as calorie availability per capital declines.
Overall, a loss of food security at high prob-
ability levels of diarrhea has a greater impact
on nutrition than does a loss of food security at
low probability levels of diarrhea.

In summary, the inclusion of the interaction
term, particularly for the Ethiopia and Pakistan data,
makes a difference to the interpretation of the pre-
schooler nutrition regression results. At low calorie
availability per capital, the inclusion of the interac-
tion term indicates that weight-for-age is more sen-
sitive to improved morbidity than one would expect
without the term. This result holds for the Ethiopia
and Pakistan data. Similarly, at high morbidity, the
inclusion of the interaction term indicates that
weight-for-age is more sensitive to changes in
household calorie availability than one would ex-
pect without the term. This result also holds for the
Ethiopia and Pakistan data.

Caveats to the Multivariate Analysis
The main contributions of the empirical analysis
presented here are (1) the comparability of the three
data sets in terms of measurement concepts, ques-
tionnaire design, and variable definition," (2) the
use of an interaction term between calories and


diarrhea, and (3) the exposition of the results in
terms of predictions of preschooler Z-score weight-
for-age.
The analysis does, however, suffer from many
of the shortcomings of other studies in its genre:
(1) the use of self-reported measures on morbidity,12
(2) the use of food availability as opposed to food
intake data,13 (3) the focus on calories as opposed to
nutrients, and (4) the imprecise measures of feeding
and hygiene practices (although mother's education
serves as a partial proxy for some of these meas-
ures). These shortcomings point the way for the next
wave of economics-of-nutrition studies in terms of
improving measurement, statistical techniques, and
conceptual models of nutrition.


Implications for Future Research
and Programs
The empirical analysis shows that (1) the exclusion
of an interaction term understates the combined im-
pact of improved household calorie availability and
reduced diarrhea probability on preschooler Z-score
weight-for-height, (2) increased diarrhea preva-
lence at low levels of household calorie availability
per capital has a greater negative impact on nutrition
than does increased diarrhea prevalence at high lev-
els of household calorie availability per capital, and
(3) a loss of food security at high levels of diarrhea
probability has a greater impact on nutrition than
does a loss of food security at low levels of diarrhea
probability.
In short, the consequences of loss of food secu-
rity are greatest at high levels of diarrhea, and the
consequences of diarrhea are greatest at low levels
of calorie availability. While this analysis is an im-
provement over past attempts to look at the relative
importance of these two pillars of good nutrition,
the research is, at this stage, only suggestive. But
what does it suggest?
The research suggests that nutrition and food
security researchers-and possibly practitioners-
often overlook the synergy that exists between the


"The comparability, however, could be improved; see, for example, the different recall periods for self-reported morbidity in Appendix Table 4.
12The errors on self-reported morbidity tend to be positively associated with income and education and are, therefore, extremely difficult to take
account of in regression analyses.
"However, a comparison of similar results from the Philippine data set (not reported here) using calorie intake and calorie availability at the
household level shows little difference between the two measures.











various inputs into nutrition. Researchers and, to a
lesser extent, practitioners tend to have a single focus,
be it on food or health or care. Households cannot
afford to have this single focus. Households realize
that the achievement of food security is not necessar-
ily a step up the ladder toward good nutrition.

Trends That Will Affect the Synergies
between Household Food Security
and Morbidity
A number of emerging trends threaten the ability of
households to exploit these synergies. It is therefore
imperative that policies and programs preserve and
enhance the ability of households in this regard.
In theory, market liberalization should enhance
a household's ability to exploit synergies by helping
to de-link choices made in production and consump-
tion activities. In the presence of well-functioning
markets for goods and labor, household resources
can be combined so as to maximize income, which
can then be spent in a way that exploits synergisms
so as to maximize utility and health. However, the
data described in Table 1 indicate that increased
income and food security do not necessarily in-
crease entitlements to health inputs, perhaps be-
cause the latter are usually quasi-public goods. Mar-
kets may not exist for health care, drinking water,
sanitation, and child care. Such goods require in-
vestments at a scale that is beyond a household's
reach. In sum, markets may be "missing" for many
of the nonfood inputs into nutrition.
A number of trends threaten to increase the
negative consequences of these missing markets be-
fore the markets have time to develop. First, urbani-
zation combined with poverty and in the absence of
strong family and formal safety nets may increase
the premium on good health relative to the premium
on food security (see Schultz and Tansel 1995).
Increasingly, urban households have to rely on good
health to keep wage earners in jobs. In the rural
areas the labor provided by these wage earners may
have been substituted for by other family members.
Second, lack of water markets and increasing com-
petition for water between production (both agricul-
tural and industrial) and consumption mean that
production and nutrition decisions related to water
are increasingly linked at the community and house-
hold level. Third, increasing rates of HIV/AIDS
devastate the productive capacity of the poor in


rural and urban areas, but because of the political
power of urban communities, public expenditures
may be pulled away from preventative health care
infrastructure toward expensive urban health care
that is curative in nature (see Brown, Webb, and
Haddad 1994). Finally, the increased need for health
and sanitation services due to the surge in the
number and size of displaced populations will place
.increasing pressure on social service budgets, espe-
cially against the backdrop of economic reform and
adjustment.
These trends raise several questions regarding
practical approaches:

Why have past multisectoral agriculture and
health programs generally failed, and what
lessons can be learned from successful ones?

Why have rural markets for basic services
been slow to develop, and what are the con-
straints? Does an expanding private sector
improve access to nonfood nutrition inputs
for the poor?

How can marginalized population groups,
such as women, gain improved access to food
and nonfood inputs? How can trade-offs due
to resource constraints be minimized?

To answer these questions, conceptual, analyti-
cal, and policy work on the many facets of food and
nonfood interactions should be expanded.

Projects and Policies That Show
Promise for Preserving and Enhancing
the Synergies
Designing projects and policies so that households
do not have to choose between credit and education,
between food production and child vaccinations,
and between income generation and child care is
extremely difficult. Programs that combine credit
services with health and nutrition programs run the
risk that those interested in credit access with high
opportunity costs are not those most interested in
nutrition education. One study suggests that women
with more binding time constraints (as proxied by
having preschoolers) were able to get access to
project credit but, it is deduced, were less able to use
it effectively (De Groote et al. 1994). To be fully











effective in improving food security, projects such
as these have to reduce the time burdens on partici-
pating women. The trade-offs between income gen-
eration and child care time may be blunted by a
program design more attuned to participants' child
care needs. Similarly, it is important to better docu-
ment the effects that time saved as a result of im-
proved water access have on child nutrition and
survival; for example, do the synergisms between
improved water quality and time freed up for activi-
ties such as child care exist in the field (Burger and
Esrey 1995)? Exploiting these synergisms offers
high potential rewards, in terms of maximizing not
only synergies in delivery, but also synergies in the
impact on preschooler nutrition.
In many ways, this synergistic approach is a
response to the calls for "linking relief and develop-
ment" and for a "relief-to-development continuum,"
which have recently gained a high profile (Linking
relief and development 1994). These phrases repre-
sent the sentiment that, whenever necessary and
possible, development interventions should use re-
sources more effectively by providing relief while
working to decrease the need for relief during the
next round of shocks. The following interventions
may be successful in this regard: labor-intensive
public works programs that improve health and edu-
cation infrastructure, innovative savings schemes
that allow households to manage consumption in
order to protect production capabilities, and credit
schemes targeted to women and including a health
and nutrition education component. Furthermore,
these projects can be intertwined to create safety
nets for the poor that not only catch those who fall,
but also make sure they get back up.
The potential for these programs to succeed is
clear from some recent research. For example,
Webb et al. (1994) conclude that while most public
works projects in Ethiopia are based on soil conser-
vation or reforestation objectives, these activities
are among the least desired by the participants
themselves. They find that the participants most
desire public works projects that are related to
health and sanitation, such as health clinic construc-
tion, piped water, and latrine building.


Postscript: Is There a Definitional
Continuum between Food
Security and Nutrition, and
Is It Useful?
The programs described here aim at raising the entitle-
ment to food and simultaneously providing the non-
food inputs that increased income does not necessarily
provide. Food staves off hunger and, when combined
with nonfood inputs, gives individuals a better chance
of surviving the next shock and of getting on with
development. As such, these programs embody the
idea of linking relief and development.
Definitions of food security center on the con-
cept of entitlement to food. However, the entitle-
ment concept is usually only applied before the
dietary intake of that food. At high levels of morbid-
ity, the body's ability to utilize consumed food di-
minishes: the body's physiological or internal enti-
tlement to food is diminished, even in the presence
of adequate external entitlements. Even if the pur-
chasing power of the household is high, the process-
ing power of the body may be low. It may be worth-
while to broaden the concept of food security to
include the idea of an internal entitlement.
What is gained from broadening the concept of
food security, and what is lost? Certainly if the
concept of food security were broadened, it could
run the risk of losing its focus and its political and
institutional constituency. However, broadening the
concept strengthens the focus on entitlements. Fur-
thermore, this broader concept provides an entry
point for nutritionists who are interested in house-
hold food security.
What else is gained? Greater emphasis is placed
on the idea that solutions to household food insecu-
rity and malnutrition are linked. This paper has ar-
gued that a number of emerging trends threaten the
ability of households to exploit positive synergisms
for nutrition status arising from food and nonfood
consumption. Policy and programs should strive to
preserve and enhance these synergisms by seeking
to simultaneously increase external and internal en-
titlements to food.






















Appendix


Table 3-Summary of IFPRI work on determinants of anthropometric status

Study Country Main Findings Comments


Alderman and Garcia
1993



Teklu, von Braun,
and Zaki 1991


von Braun, de Haen,
and Blanken 1991



Bouis and Haddad
1990

Kennedy 1989


von Braun, Puetz, and
Webb 1989


von Braun, Hotchkiss,
and Immink 1989



Kennedy and Cogill
1987

Pinstrup-Andersen and
Garcia 1987


Kumar 1979


Pakistan




Sudan



Rwanda


Preschooler anthropometric status was not con-
strained by low household food availability, but by
poor health and high levels of infection (-). Vitamin
A consumption (+) from household food expenditure
data had large impact on preschooler WH.
Diarrhea (-), household expenditures on grain (+),
< 3 meals/day (-), mother's literacy (++), father's
literacy (+) had significant impacts on WA of
preschooler.
Household calories from food expenditures (+ for
ZHA, ZWA, ZWH), heavy worm load (- for ZHA),
clean toilet (+ for ZHA, ZWA), birth order (- for
ZHA, ZWA, ZWH) had significant impacts on
preschooler anthropometrics.


The Philippines Preschooler calorie intake (+), diarrhea and fever
(-), mother's education (+) had significant impacts
on preschooler WH.


Kenya


The Gambia



Guatemala




Kenya


The Philippines



India


Preschooler calorie intake (+), diarrhea (-) had
significant impacts on preschooler WA and HA (but
not WH).
Preschooler calorie intake (+), diarrhea (-), water
quality (-), child of compound head (+) had
significant impacts on preschooler WA and HA (but
not, in general, on WH).
Per capital income (+), share of male nonagricultural
income (+), share of female nonagricultural income
(++), duration of breast-feeding (+) had significant
impacts on preschooler WA and HA (but for
WH, only income effects +).
Diarrhea (- for ZWA, ZWH), female head of
household (+ for ZWA, ZHA), calories of child
(+ for ZHA, ZWA) had significant effects.
Household income (+ for WA), diarrhea (- for WA),
poor water quality (- for WA and HA), birth order
(-), nutrition education (+ for WA and HA) had
significant effects.
Rice ration subsidy (+), per capital own-farm income
(+) had significant effects.


No food intake data were collected.
Also, no individual food intake data were
collected. No correction was made for
unobserved heterogeneity.

Diarrhea and household grain expenditure
are instrumented. No food intake data
were collected. No correction was made
for unobserved heterogeneity.
None of the endogenous explanatory
variables are instrumented. No correction
was made for unobserved heterogeneity.


None of the endogenous explanatory
variables are instrumented. No correction
was made for unobserved heterogeneity.
None of the endogenous explanatory
variables are instrumented. No correction
was made for unobserved heterogeneity.
None of the endogenous explanatory
variables are instrumented. No correction
was made for unobserved heterogeneity.

None of the endogenous explanatory
variables are instrumented. No correction
was made for unobserved heterogeneity.


None of the endogenous explanatory
variables are instrumented. No correction
was made for unobserved heterogeneity.
None of the endogenous explanatory
variables are instrumented. No correction
was made for unobserved heterogeneity.

None of the endogenous explanatory
variables are instrumented. No correction
was made for unobserved heterogeneity.


Notes: WA = weight-for-age, ZWA = Z-score weight-for-age, HA = height-for-age, ZHA = Z-score height-for-age, WH = weight-for-height, ZWH =
Z-score weight-for-height, (-) = negative effect on anthropometry, (+) = positive effect on anthropometry.








12




Table 4-Brief description of data sets

Survey Number of Recall Period
Country Year Households for Morbidity Brief Description


Bangladesh 1991/1992 737 3 months A food consumption and nutrition survey was carried out three times over
a one-year period to assess the consumption and nutrition effects of two
targeted food intervention programs-Rural Rationing and Vulnerable
Group Development Program. The survey was conducted in eight villages
in eight thanas (districts) located in four divisions of the country. In
selecting the sample, focus was on achieving adequate variation in factors
such as distress proneness and developed and undeveloped areas. Besides
food intakes and socioeconomic data, information on individual health,
anthropometric measures, sanitation, and housing conditions was also
collected. Questions asked on morbidity status were: Has any member of
the family suffered from the following list of diseases during the past three
months? If yes, days sick/whom consulted/if no consultation, why not?
(Ahmed 1993).
Ethiopia 8/1990 to 650 2 weeks The data were collected from 11 rural Peasant Associations (PAs) in
9/1991 Sike, Omo, and Alaba awarjas in South Sewa Province, roughly once a
month from August 1990 through September 1991. The focus of the study
was to identify seasonal swings in nutritional and health status and the
determinants of the seasonal swings, with a particular emphasis on
seasonality in agriculture production, food availability, and labor demand.
The sample frame was designed to capture the main characteristics that
influence the seasonal swings. The four key factors considered were
ecological zones, availability of public works programs, infrastructure
access, and ethnic origin. Ten PAs were selected based on the above
characteristics, and one additional site was drawn from the worst part of
the region for comparison purposes. Forty households from each of the 10
PAs were randomly selected from three economic strata constructed for
each of the PAs.
Anthropometric measures of weight and height were taken for all
members of households. Information on water and sanitation was also
collected. The questions specifically asked on morbidity were: "Were any
members of the household sick from malaria, measles, jaundice, cough,
whooping cough, injuries, generalized ache, cough with fevers, and other?
... If yes, please specify days sick/type of treatment sought, if any"
(Kumar, Bhattarai, and Amde 1995).
Ghana 1992/1993 610 1 week A survey was conducted to evaluate the effects of credit schemes targeted
to women on household income, household food security, and the nutrition
status of women and preschoolers. Two surveys were conducted in two
regions, Volta and Brong-Ahafo, to capture the major types of credit
schemes. Once the villages were selected that had access to the credit
scheme, a random sample of households was drawn from a census of
households in each of these villages. Questions on morbidity included
"... number of days sick by disease type" (Kennedy et al. 1994).
Guatemala 1991 376 1 week A study was conducted in 1991 in six villages as a follow-up to a similar
study conducted in 1985 in the same villages. The study was undertaken to
examine the long-term effects of crop diversification and
commercialization, particularly of nontraditional crops, on production,
income, food security, health, and nutrition in small-farm households. An
attempt was made to include as many of the families from the 1985 study
as possible. This resulted in a first-stage sample of 339 families, to which
was added 176 families selected at random from the six villages.
Households with highly incomplete data were eliminated, bringing the
total household sample size to 376. Besides socioeconomic data,
information on water, housing, sanitary conditions, and morbidity was
collected. One child under 6 years old and one between 6 and 13 years of
age were selected at random in each family for anthropometric measures.
Weight and height were also taken for the spouse and/or adults 18 and
above. Questions specifically on morbidity included ".. was any child
sick from diarrhea, fever, vomiting, coughing, infections (measles,
chicken pox, mumps, hepatitis, etc.)? If yes, number of days sick, type of
treatment sought" (Immink et al. 1993).














Table 4-Continued

Survey Number of Recall Period
Country Year Households for Morbidity Brief Description


Kenya 1992 454 7 days This study was carried out to examine the feasibility of integrating
nutrition and food security components into a commercial agriculture
scheme in southwest Kenya and to design and implement a local-level
monitoring system in conjunction with the district-level government.
Previous work in this area concluded that income-generating agriculture
projects must be combined with other nutrition and food security
components in order to decrease malnutrition and improve nutrition status.
The study was conducted in Nyanza Province and South Nyanza
District.The households selected represented a re-surveyed random sample
(Kennedy et al. 1995).
Pakistan 7/1986 to 880 2 weeks This panel survey involving 12 visits was carried out in 1 well- developed
9/1991 and 3 poorly developed districts selected from four provinces to analyze
different dimensions of poverty. The provinces and districts were selected
purposively. The selected districts were Attock in Punjab, Badin in Sindh,
Dir in North West Province, and Mastung/Kalat in Baluchistan. The
villages and households in each district were selected by stratified random
sampling. Along with socioeconomic data, the survey collected
anthropometry and health data for children below 6 years of age in all 12
rounds and for adults in 5 of the 12 rounds. In each of the 12 rounds,
observations on health conditions, specifically diarrhea and other
illness, were recorded for each child under 6 years old. Information on
water and sanitation was also collected. Questions on morbidity included:
Did you have diarrhea in the past two weeks? Who did you first see? How
many days sick? Did you have other illness? If yes, how many days sick?
Whom did you see? Cost for treatment? Access to health services/travel
time? One important finding: The results indicate that at the household
level in rural areas, children respond more strongly at the margin to health
inputs than to food availability (Alderman and Garcia 1993).
The Philippines 1984/85 448 2 weeks A total of 448 corn- and sugar-producing households were surveyed four
times at four-month intervals in Bukidnon Province, Mindanao. The
sample included smallholder landowners, tenants, and landless laborers.
Data were collected on landholdings, income sources, expenditure
patterns, calorie intakes, and nutritional and morbidity status (Bouis and
Haddad 1990).
Zambia 1986 330 1 month A survey was carried out in the Eastern Province of Zambia to examine
the growth and equity effects of technology change in agriculture. Study
sites were located in each of the districts and were selected to provide a
representative sample from the province and its two main ecological
zones-plateau and valley. The selection of households was by stratified
random sampling. A total of 330 households were selected and visited
monthly during 1986. Information on agricultural production practices,
labor allocation, off-farm income, food and nonfood consumption, and
health and sanitation was collected. Anthropometric measures (weight and
height) were taken for each individual in the household at four times
during the year. Information on water and sanitation was also collected.
On health, participants were asked, "Were you or any member ever
sick from the following illnesses during the last month-diarrhea,
respiratory infection, measles, generalized acute illness, chronic mild,
chronic serious illnesses?" The survey also asked the number of days sick
and the type of treatment sought for each of the illnesses (Kumar 1994).









14




Table 5-Regression summary of preschoolers, Ethiopia

Household Preschooler
Calorie Availability Probability of
per Capita Diarrhea (1 = yes)

Variable Wald
Variable Name Coefficient t-statistic Coefficient Statistic

Predicted preschooler diarrhea YDIARFI
Predicted household calorie availability per capital YCAL
Interaction: predicted calorie availability x
predicted diarrhea CALDIAR
Household size FMSIZE -35.471 -2.36* 0.152 1.63
Number of males in household 15-65 years old HM1565 -24.625 -1.10 0.099 0.30
Number of females in household 15-65 years old HF1565 41.217 1.58 -0.031 0.02
Number of children in household 6-14 years old HKID614 -77.844 -3.67* -0.127 0.47
Number of adults greater than 65 years old HAD65 -215.328 -3.96* -0.645 1.51
Season: 1 = round 10, 0 = round 6 SEASON 0.043 0.00 -1.230 10.67*
Location: 1 = highland, 0 = others ECOLI -0.849 0.46
Location: 1 = lowland, 0 = others ECOL2 -1.267 4.07*
Sanitation (latrine present: 1 = yes) SANIT -0.250 0.09
Piped water: 1 = yes, 0 = other DUMWATI -2.688 0.04
Protected spring/well: 1 = yes, 0 = other DUMWAT2 -0.399 0.28
Unprotected spring/open well: 1 = yes DUMWAT3 -0.156 0.15
Distance to nearest health services (kilometers) DISTHS 0.247 1.11
Nearest health post: 1 = yes, 0 = no HPDUMMY -0.513 0.58
Nearest health center: 1 = yes, 0 = no HCDUMMY -0.628 0.18
Nearest clinic: 1 = yes, 0 = no CLDUMMY -3.263 2.16
Price of maize (rs per kilogram) MZPRC79 -255.942 -3.25*
Land owned (hectares) LANDTOTH 112.721 7.91* -0.002 0.0001
Livestock value LSIVAL 0.346 11.78* -0.001 2.09
Mother's height (centimeters) MOMHT
Height squared HTSQ
Education of household head (grades) HHEDUCAT 0.012 0.03
Education of mother (grades) MOMED 7.484 0.41 0.066 0.17
Age of individual (months) NAGEM
Age squared AGESQ
Age group: 1 = < 2 years, 0 = > 2 years AGEGR 0.833 6.58*
Sex of individual: 1 = male, 0 = female SEXE -0.036 0.01
Sex of household head: 1 = male, 0 = female HHSEX
Marital status: 1 = polygamous MARDUM2 240.269 4.49*

Constant 2,261.961 20.58 -2.698 5.19
Adjusted R-squared 0.270
F 42.400
-2 log likelihood 327.60
Model chi-square (p < x) 62.4(.000)

Notes: indicates significant at .05 level. ** indicates significant at.10 level.
















Preschooler Z-Score Preschooler Z-Score
Weight-for-Age Weight-for-Age (1 = > -2)
(1 = > -2) (with interaction)

Wald Wald
Coefficient Statistic Coefficient Statistic


-4.262
0.001



-0.087
0.154
0.256
0.063
-0.058
-0.140
-0.107
-0.128

















-0.083
0.000
0.008
0.009
-0.066
0.001


-0.050
0.530


2.610


4.195*
6.354*



2.529
3.599*
6.851*
0.559
0.074
0.770
0.220
0.284

















0.291
0.566
0.047
0.013
9.625*
7.170*


0.122
2.041


0.050


-23.280
0.000

0.010
-0.098
0.165
0.264
0.090
0.015
-0.093
-0.054
-0.093

















-0.108
0.001
0.007
0.017
-0.062
0.001


-0.062
0.546


4.600


3.897*
3.097**

2.724**
3.200**
4.096*
7.203*
1.099
0.005
0.328
0.054
0.147

















0.493*
0.838
0.036
0.050
8.607*
6.583*


0.189
2.158


0.160


1,165.70
63.03(.000)


1,162.90
65.8(.000)









16




Table 6-Regression summary of preschoolers, Pakistan

Household Preschooler
Calorie Availability Probability of
per Capita Diarrhea (1 = yes)

Variable Wald
Variable Name Coefficient t-statistic Coefficient Statistic

Predicted preschooler diarrhea YDIARFI
Predicted household calorie availability per capital YCAL
Interaction: predicted calorie availability x predicted
diarrhea CALDIAR
Household size HSIZE -83.203 -13.00* -0.2722 15.16*
Number of males in household greater than 16 years old MADULT 99.319 11.18* 0.3815 16.62*
Number of females in household greater than 16 years old FADULT 107.533 11.26* 0.2775 8.28*
Number of males in household 6-15 years old SCHOOLM 55.801 7.12* 0.254 9.76*
Number of females in household 6-15 years old SCHOOLF 52.660 6.78* 0.2251 7.77*
Total children 5-16 years old SCHOOL
Number of children in household under 6 years old PRESCH F -8.090 -1.05 0.1445 2.44
Season: 1 = round 10, 0 = round 6 SEASON 4.199 0.31 -0.8947 54.80*
Location dummy district I DUMDISTI 86.394 3.80* -1.8069 30.52*
Location dummy district 2 DUMDIST2 346.847 13.72* -2.1333 36.29*
Location dummy district 3 DUMDIST3 32.045 1.91* -1.8273 23.68*
Sanitation (latrine present: 1= yes) DUMLAT -0.325 2.40
Tap water: 1 = yes, 0 = other sources DUMWATI -0.0165 0.01
Hand pump: 1 = yes, 0 = other sources DUMWAT2 0.0889 0.19
Well: 1 = yes, 0 = other sources DUMWAT3 -0.159 0.60
Village-level distance to government TGOV2 0.0011 0.30
Knowledge about rural health unit: 1 = yes KHUN -0.7999 6.68*
Price of wheat (rs per kilogram) WHPRICE -5.418 -0.34
Land owned (acres) LANDTOTH 1.971 2.82*
Asset value (rs) ASSETVAL 0.0001 2.32*
Education of household head (years) EDUCHH 0.0039 0.06
Education of adult female (years) FEMEDUC 7.575 2.84* 0.0232 1.01
Age of individual (months) AGEMOADJ 0.0083 0.38
Age squared AGESQ -0.0005 4.29*
Sex of individual: 1 = male, 0 = female SEX 0.1352 0.91
Sex of household head: 1 = male, 0 = female SEXHH -0.2952 0.13
Land x district dummy 1 LAND1 39.800 15.28*
Land x district dummy 2 LAND2 4.811 4.11*
Land x district dummy 3 LAND3 3.225 4.13*

Constant 2,141.746 50.79* 1.5596 2.77**
Adjusted R-squared 0.49
F 112.6
-2 log likelihood 1,882.7
Model chi-square (p < x) 205.5(.000)

Notes: indicates significant at .05 level. ** indicates significant at .10 level.
















Preschooler Z-Score Preschooler Z-Score
Weight-for-Age Weight-for-Age (1 = > -2)
(1 = > -2) (with interaction)

Wald Wald
Coefficient Statistic Coefficient Statistic


-1.5108
0.0006



-0.0066






0.0314
0.058
0.166
-0.782
-1.830
-1.800















0.055
0.003


0.019







0.350



2,346.5
246.8(.000)


2.80**
2.80**



0.15






0.89
0.69
1.20
8.80*
30.40*
81.30*















5.60*
0.81


0.02







0.21


-9.8125
-0.00006

0.004
-0.0096






0.0407
0.069
0.150
-0.860
-1.800
-1.870















0.051
0.003


0.026







1.750


4.80*
0.02

3.60*
0.31






1.5
0.98
0.92
10.20*
30.40*
83.60*















4.70*
0.61


0.05







2.70**


2342.9
250.4(.000)










18




Table 7-Regression summary of preschoolers, the Philippines

Household Calorie
Availability per Capita Household Calorie Intake
(MCALPC) per Capita (HCALPC)

Variable
Variable Name Coefficientt t-statistic Coefficient t-statistic


Predicted preschooler diarrhea
Predicted household calorie availability per capital
Interaction: predicted calorie availability x predicted
diarrhea
Household size
Number of males in household 5-17 years old
Number of females in household 5-17 years old
Number of males in household greater than 17 years old
Number of females in household greater than 17 years old
Number of male children in household under 5 years old
Total school-age children 5-17 years old
Total adults greater than 17 years old
Round dummy 1
Round dummy 2
Round dummy 3
Latrine dummy 1: 1 = water sealed/flush
Latrine dummy 2: 1 = open/bushes
Piped water/tank: 1 = yes, 0 = other sources
Artesian
Well: 1 = yes, 0 = other sources
Water boiled for children: 1 = yes, 0 = no
Distance to nearest doctor (minutes)
Price of rice (peso per kilogram)
Price of corn (peso per kilogram)
Land owned (acres)
Average net worth of household assets
Mother's height (centimeters)
Education of father (years)
Education of mother (years)
Age of individual (months)
Age squared
Sex of individual: I = male, 0 = female


Constant
Adjusted R-squared


YDIARFI
YMCAL


CALDIAR2
NUMRD
MALE517
FEML517
MALEGT17
FEMLGT17
MALE05
SCHOOL
ADL
RDI
RD2
RD3
DUMLATI
DUMLAT2
DUMWATI
DUMWAT2
DUMWAT3
KIDBOIL
TIMEDOC
PRICE
PRCORN
FARMSZ
AVNETWTH
AVHTMOTH
FATED
MOMED
AGEMO
AGESQ
SEX


-173.750
94.528
108.717
151.279
24.376
43.546



114.190
-11.857
199.559











-85.470
1.654
20.516
0.003


-13.37*
6.14*
7.08*
7.62*
1.05
3.21*



3.83*
-0.36
4.83*











-5.54*
0.10
4.76*
7.00*


-0.119 -0.06


2,705.04
0.17
43.8


22.03*


-2 log likelihood
Model chi-square (p < x)

Notes: indicates significant at .05 level. ** indicates significant at .10 level.


-107.160
87.575
58.857
161.785
47.706
7.487



230.003
18.039
-101.070











19.897
7.467
11.646
0.001



-0.525





1,751.17
0.09
22.4


-8.76*
6.04*
4.07*
8.66*
2.18*
0.59



8.20*
0.57
-2.60*











1.37
0.48
2.87*
1.46



-0.30





15.16*

















Preschooler Z-Score
Preschooler Probability of Preschooler Z-Score Weight-for-Age (1 = > -2)
Diarrhea (1 = yes) Weight-for-Age (1 = > -2) (with interaction)

Wald Wald Wald
Coefficient Statistic Coefficient Statistic Coefficient Statistic


0.094
0.023
-0.066
-0.520
0.188
-0.267



-0.183
0.180
0.404
0.169
0.196
1.044
0.900
0.067
-0.312
-0.005


0.41
0.02
0.15
3.70*
0.48
1.66



0.27
0.31
1.66
0.17
0.37
0.12*
7.52*
0.06
0.70
1.08


-3.271
0.001



0.013








0.054
-0.076
-0.083
0.060
0.006


3.49**
8.56*



0.04








0.67
0.94
0.31
0.17
0.00


-20.501
0.001

0.010
0.015








0.051
-0.073
-0.081
0.052
-0.005


-0.081 2.04
0.000003 0.31


0.082


0.009
0.029


60.17*


1.28
34.83*


-1.161 113.43*

-13.259 58.92*


2,388.200
289.3(.000)


-0.034
-0.001
-0.089
0.001
0.533

-1.256


0.65
0.01
6.52*
1.52
3.07**

1.94


0.083


0.007
0.029


-1.152

-12.667


60.40*


0.87
34.88*


111.52

52.49*


694.8
82.9(.000)


2,384.600
292(.000)

















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Lawrence Haddad is director of the Food Consumption and Nutrition Division, Saroj Bhattarai
is a research analyst, and Shubh Kumar is a visiting research fellow at the International Food
Policy Research Institute. Maarten Immink, formerly a research fellow at IFPRI, works for
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Food, Agriculture, and the Environment Discussion Papers


1. Alleviating Poverty, Intensifying Agriculture, and Effectively Managing
Natural Resources, by Per Pinstrup-Andersen and Rajul Pandya-Lorch, 1994

2. Sociopolitical Effects of New Biotechnologies in Developing Countries, by
Klaus M. Leisinger, 1995

3. Africa's Changing Agricultural Development Strategies: Past and Present
Paradigms as a Guide to the Future, by Christopher L. Delgado, 1995

4. A 2020 Vision for Food, Agriculture, and the Environment in Sub-Saharan
Africa, edited by Ousmane Badiane and Christopher L. Delgado, 1995

5. Global Food Projections to 2020, by Mark Rosegrant, Mercedita Agcaoili-
Sombilla, and Nicostrato Perez, 1995

6. A 2020 Vision for Food, Agriculture, and the Environment in Latin America,
edited by James L. Garrett, 1995

7. Agriculture, Trade, and Regionalism in South Asia, by Dean A. DeRosa and
Kumaresan Govindan, 1995

8. Major Natural Resource Management Concerns in South Asia, by Gerard J.
Gill, 1995

9. Agriculture, Technological Change, and the Environment in Latin America:
A 2020 Perspective, by Eduardo J. Trigo, 1995

10. Overcoming Malnutrition: Is There an Ecoregional Dimension? by Manohar
Sharma, Marito Garcia, Aamir Qureshi, and Lynn Brown, 1996

11. Structural Changes in the Demand for Food in Asia, by Jikun Huang and
Howarth Bouis, 1996

12. Middle East Water Conflicts and Directions for Conflict Resolution, by
Aaron T. Wolf, 1996

13. The Transition in the Contribution of Living Aquatic Resources to Food
Security, by Meryl Williams, 1996

14. Land Degradation in the Developing World: Implications for Food, Agricul-
ture, and the Environment to the Year 2020, by Sara J. Scherr and Satya
Yadav, 1996

15. Potential Impact of AIDS on Population and Economic Growth Rates, by
Lynn Brown, 1996















































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