OSIPP Discussion Paper : DP-2009-E-007

Economic analysis on the socioeconomic determinants of childmalnutrition in Lao PDR

October 9, 2009

Yusuke Kamiya

Ph.D candidate, Osaka School of International Public Policy (OSIPP)【Keywords】Child malnutrition, Anthropometrics, Stunting, Wasting, Underweight,
Social determinants of health, Millennium Development Goal (MDG), Health policies,
Lao PDR (Laos)【JEL classification】I18, O15, O21【Abstract】The prevalence of stunting and underweight among Lao children is amongst
the highest in the region. This paper provides a theoretical framework which integrates
the mechanism of child malnutrition and a household decision-making behaviour andinvestigates the relationship between socioeconomic factors and child health outcomes.
Using the Lao Multiple Indicator Cluster Survey 3 dataset, it reveals that mother’s age
and education level, ethnicity, household assets and community factors such as water,
sanitation and communication infrastructure have a statistically significant impact onchild nutritional status. The unobserved heterogeneities of both household andcommunity are also found to be associated with child nutrition production.
Address: Osaka School of International Public Policy, Osaka University, 1-31 Machikaneyama,
Toyonaka, Osaka 560-0043 Japan, Tel/Fax: (81)6-6850-5839, E-mail address:
y-kamiya@osipp.osaka-u.ac.jp
I acknowledge Professor Naoto Yamauchi, Osaka School of International Public Policy, for
permitting me to publish this paper. I am grateful to Dr Kara Hanson, London School of Hygiene
and Tropical Medicine, for her insightful advice and comments.

11. Introduction
Chronic malnutrition1 in children is highly prevalent and remains a global challenge.
178 million and 112 million of children under-five are stunted (below -2 height-for-age
Z-scores) and underweight (below -2 weight-for-age Z-scores) in developing countries
respectively (1). The Millennium Development Goal (MDG) 1 “Eradicate extreme
poverty and hunger” addresses reducing the proportion of children who are underweightby half between 1990 and 2015. Since malnutrition is an underlying cause of an
estimated more than a half of all under-five deaths (2, 3), the improvement ofmalnutrition will also assist in the goal to reduce child mortality (MDG 4).
It has long been recognised that socioeconomic factors such as economic
growth, poverty, water and sanitation, education and gender are important determinantsof health outcomes (4-12). Thomas and Frankenberg 2002, who review bothexperimental and observational studies on this issue, state that there is abundantevidence at both the microeconomic and macroeconomic level that a variety of healthindicators are positively associated with different dimensions of economic prosperity
and the causal pathways linking health and economic outcomes run in both directions––
with higher income, individuals can invest more in their health, or healthier workers are
more productive and can enjoy higher wages–– (13).

1Malnutrition has a meaning both for undernutrition and overnutrition. In this paper, the term refers only
to cases of undernutrition.

21.1. Returns to improving child nutrition
Nutritional status during childhood affects every phase of their life and therefore many
international agencies have launched strategies and reports addressing the significanceof focusing on child malnutrition (5, 14-18). Investment in child nutrition contributes
not only to improving their current welfare but to enhancing their opportunities overtheir life cycles. There is a huge amount of literature on this issue (for review papers
see: 19-25).
Importantly, malnourished children are much more likely to die due to acommon childhood disease than those who are sufficiently nourished. Many studies
have shown that malnutrition is highly associated with high child mortality (26-29) andtherefore child survival could be accelerated by reducing the general level ofmalnutrition. Furthermore, the risk of mortality is inversely related to anthropometricindices of child nutritional status and malnutrition has a ‘potentiating’ effect onmortality rather than additive fashion (30, 31).
On the other hand, better nutrition during early childhood improves educational
attainment (32-37) and post-schooling performances (38). Amongst recent studies,
Hoddinott et al. 2008 find that a nutrition intervention in early childhood (foodsupplementation during 0-24 months) led to 46% increase in the average wage in themale sample in Guatemalan villages (39). Carba et al. 2009 using the longitudinalsurvey in the Philippines confirm that higher height-for-age Z-scores at age 2 are
3significantly associated with a 40% increase in the likelihood of being engaged in‘formal work’, compared to ‘no job’ in the male sample population (40).2

1.2. Socioeconomic and health situation in Lao PDR
The Lao People’s Democratic Republic (Lao PDR) has a population of 5.6 million and asparse population density. There are 49 officially recognized ethnic groups (42, 43).
52.5% of the total population is ethnic Lao, the principal lowland inhabitants and thepolitically and culturally dominant group. The other ethnic minorities predominate in
the highlands. Most of the population are dependent on subsistence rice production andtherefore food security is a serious concern (44). While the official poverty rate dropped
from 39% in 1997 to 33.5% in 2002 (45), Laos ranked 133th out of 179 nations on the
Human Development Index in 2006 (46). The Lao National Growth and Poverty
Eradication Strategy (NGPES), which aims to reduce poverty through strong economic
growth, focuses on 47 poorest districts identified by the Government of Laos (GoL) as
priority districts for poverty alleviation (47).
Lao PDR reduced its under-five mortality rate (U5MR) from 170 per 1000 live
births in 1995 to 75 deaths per 1000 live births in 2006, but the figure is much worse
than neighbouring Thailand and Vietnam (48)3. In addition, the prevalence of childmalnutrition is high. During the period 2000–2007 Lao PDR experienced the worst
2

Another example is that exposure to China’s 1959-61 famine in early life led to shorter stature andlower earnings (41).
3
The U5MR is 8 deaths per 1000 live births in Thailand and 17 deaths per 1000 live births in Vietnam
respectively.

4

prevalence of stunting (40%) and underweight (37%) in children under-five in theregion (Figure 1).

Figure 1: Prevalence of malnutrition among children under-five (2000-2007)
50%
37%

40%
30% 30%40%

36% 37%

32%

32%
28%

30%
20%

20%

12%
8%

10%

6%

4%

9%

7%

7%

9%

0%
Stunting
Thailand

Viet Nam

Wasting
Phillipinnes
MyammarCambodia

Underweight
Lao PDR

Source: UNICEF 2009 (49)

The GoL committed itself to the international obligations of the “Right toAdequate Food” by ratifying the International Covenant on Economic, Social andCultural Rights (ICESCR) (50) in 2007 to ensure that all Lao citizens would be able toavail of their “fundamental right to be free from hunger”. In 2008 the GoL promulgatedthe ‘National Nutrition Policy’ (NNP)4 to synchronise the international obligations with
its national policy. The NNP aims to “accelerate the reduction of malnutrition among all
ethnic groups and decrease associated morbidity and mortality risks” (51).
A fragile health system is thought to hamper the improvement of child healthstatus in Laos5. Although there is now a global consensus that stronger health systems

4

The NNP serves as a “legally binding document to substantially reduce levels of malnutrition,
especially vulnerable groups, and to mainstream nutrition in National Socio-Economic Development
Plans in line with the implementation of the National Growth and Poverty Eradication Strategy”.
5
For instance, only 40% of infants under the age of one were immunised against measles and just 48% of
the population had access to adequate sanitation facilities in 2007 (49)5are key to achieving the MDGs (52), there is no clear answer to how a country’s healthsystem and socioeconomic factors interact with child health outcomes.
Empirical studies on the causes of child malnutrition assist in programming
effective nutrition interventions. Nevertheless, there exist a limited number of studies
which measure nutritional status of Lao children and/or examine the causes ofmalnutrition (53-58) 6 . Phengxay et al. 2007 performed a cross-sectional study inLuangprabang province to obtain anthropometric measures of 798 children andinvestigate risk factors of them. They found low maternal education, poor nutrition
knowledge and restricted intake of meats were main causes for child malnutrition (65).

1.3. Aims of the paper
As discussed above, a proper understanding of the mechanisms and determinants ofchild malnutrition is pertinent to designing a strong nutrition strategy which triggers
better child health gains and a further reduction in child mortality. Accordingly, thispaper aims to investigate the relationship between socioeconomic factors and childnutritional status, and use these findings as the basis for recommendation for effective
nutrition interventions. Specific objectives are: 1) to provide an analytical frameworkand empirical model on the determinants of child malnutrition; 2) to identify thesocioeconomic determinants of child nutritional status in Laos using a nationwide

6

The number of empirical studies is limited even in neighbouring countries (Cambodia: 59, Thailand:60-62 and Vietnam: 63, 64).

6

household survey dataset; 3) to identify the patterns of growth faltering by age among
under-five children; and 4) to suggest effective interventions to improve childnutritional status.

2. Theoretical framework
2.1. Conceptual framework of the determinants of child malnutrition
The causes of child malnutrition are diverse, multidimensional and interrelated. A
framework developed by UNICEF encompasses these complexities (Figure 2).

Figure 2: Framework of the determinants of child nutrition by U,ICEF
Child malnutrition,
death, disability

Outcomes

Immediate causes

Underlying causes(household level)

Basic causes(societal level)

Inadequate
dietary intake

Insufficient accessto food

DiseaseInadequate
maternal and childpractices

Quantity and quality ofactual resources – human,economic andorganisational – and theway they are controlled

Potential resources:
environment, technology,
peoplePoor water/
sanitation andinadequate healthservices

Inadequate and/or
inappropriate knowledge
and discriminatory
attitudes limit household
access to actual resources

Political, cultural, religious,
economic and social
systems, including women’s
status, limit the utilisationof potential resources

Source: UNICEF 1990 (15), UNICEF 1998 (14)It categorises the causes of child malnutrition into 1) immediate causes:

7

inadequate dietary intake and illness, 2) underlying causes: insufficient access to food ina household (66); inadequate health services and unhealthy environment (poor water/
sanitation and inadequate health services); and inadequate care for children and womenat household level (67), and 3) basic causes: insufficient actual resources (human,economic and organisational resource) as well as potential resources (social, politicaland environmental ) at societal level (14, 15).
In measuring the determinants of child malnutrition, two approaches have been
mainly undertaken: epidemiological approach which measures the direct association
between health inputs and outcomes and economics approach which focuses on therelationship between constraints on people’s opportunities and health outcomes (68-70).
Epidemiological approach could be statistically biased and misleading as it often does
not take into account the people’s health endowment which is not normally observed byresearchers. Considering this point, Schultz 1984 proposes a ‘stochastic framework’
which integrates the biological determinants of child health and economicallyconstrained selection of health inputs in the presence of health heterogeneity in order toobtain unbiased estimates of the determinants of child health (69). He emphasises theimportance of distinguishing observed variables such as economic endowments,
regional price, health inputs and health outcomes and unobserved variables such as
people’s preferences and biological endowment in his model structure (Figure 3).

8

Figure 3: Framework of the determinants of child malnutrition by Schultz 1984
Unobserved variables
Preferences

Biologicalendowments

Observed variables
Economic
endowments

Regional, price, andprogrammevariables

Demanded inputs or
proximatedeterminants ofhealthErrorsChild healthproduction function

ErrorsHealth outcomes

Source: Schultz 1984

2.2. Measurement of child nutritional status
Traditionally, nutrient consumption (health inputs) played a central role in measuring
and analysing child health status. However, the data of nutrient consumption are highly
likely to be subject to both random and systematic measurement errors because a
respondent’s recall method is the most common strategy for data collection (71-73).
Thus, anthropometry which directly measures people’s height and weight (healthoutcomes) has become more widely used in empirical studies on child nutrition7.
Anthropometry has a tradition going back to the 1950s and has been used tomeasure the state of child health based on their height, weight, and age8. Anthropometry
7

Strauss and Thomas 1996 argue that even if the anthropometric methods are undertaken, self-reported
height and weight are prone to systematic errors which are associated with individual’s income level.
8Child height proved to be an informative measure of child nutritional status in the 1970-80s (74, 75).

9

has advantages in assessing child malnutrition over other measures such as clinicalsigns of malnutrition or biochemical indicators because anthropometry is quite sensitive
to every spectrum of malnutrition, whereas clinical signs or biochemical indicators areuseful only at the extreme of malnutrition (26).
To measure the degree of malnutrition, anthropometric values are comparedacross individuals or populations in relation to a reference population. The internationalreference of child growth was produced by the National Center for Health Statistics(NCHS), Centers for Disease Control and Prevention (CDC) and WHO in 1978(referred to here as the ‘NCHS reference’) (76, 77). In April 2006 WHO released new
growth standards (referred to here as the ‘WHO standards’) because the NCHS
reference had been found to contain several technical limitations (78-80).
Anthropometric measures for child nutrition are expressed as Z-scores whichcorrespond to standard deviations from the median of the reference population. Z-score
for an individual i is calculated as follows:  =

where



−

is an observed value for i in a target population.

andare a medianand a standard deviation (SD) of the reference population, respectively. The following
three Z-scores are commonly used to measure child nutritional status (81, 82).
First, height-for-age Z-score (HAZ) is a longer-term index and represents linear
10

growth of a child. It gives the information about chronic malnutrition or ‘stunting’
which reflects the accumulation of past outcomes. Second, weight-for-height Z-score(WHZ) is a shorter-time index of child health status. It exhibits the situation of acutemalnutrition or ‘wasting’. Wasting is usually caused by a recent nutritional deficiency
and may manifest significant seasonal variations according to changes in the availabilityof food or disease prevalence (83). Third, weight-for-age Z-score (WAZ) is an index ofboth acute and chronic malnutrition. It provides the information about ‘underweight’.
Low weight-for-age can be due to either stunting or wasting (26)9.
Among the three indices, HAZ is usually considered as the best index torepresent children’s long-term health status because it is associated with greatermortality risk (26, 30, 84). Interestingly, there is no apparent correlation between HAZ
and WHZ, and this phenomenon has been considered as a ‘puzzle’ (85, 86). It implies
that stunting and wasting can be determined by different mechanism and therefore
separate interventions are required to improve them (87).
Anthropometry is useful not only in representing children’s health status but
also in assessing social deprivation (88). WHO recommends that height-for-age is areliable measure of overall social deprivation (89), whereas the UN’s MDG applies
weight-for-age as the main indicator of malnutrition.

9

A child whose HAZ, WHZ and WAZ is more than 2 SD below the median of the reference population
are classified as ‘moderately or severely’ stunted, wasted and underweight respectively. Those whose
WAZ, WHZ and WAZ is more than 3 SD below the median are classified as ‘severely’ stunted, wasted
and underweight respectively.

112.3. Literature review
Over the past several decades, there have been a large amount of experimental andobservational studies on the determinants of child malnutrition in developing countries.
Previous studies identified the characteristics at the level of 1) child, 2) household, and3) community as the main causes of child malnutrition. Recently, an increasing numberof studies emphasises the 4) interaction effects (complementarity and substitutability)
between the household and community factors, and 5) heterogeneities at both thehousehold and community levels because they have important policy implications.

2.3.1. Child characteristics
Sex: Many studies confirm that boys are significantly malnourished than girls (90-94)
especially in Sub-Saharan Africa after controlling for socioeconomic factors. On theother hand, Strauss 1990 and Lomperis 1991 found no gender difference in the sample
of children in Cote d’Ivoire and Colombia respectively (95, 96). In Vietnam there wasno significant gender difference in both the urban and rural area (63).
Age: There is a common age-specific pattern in nutritional status of children under-fivein developing countries: a child is shorter and weigh less for higher age-bracket andmalnutrition increases up to the age of 2 and then levels off (97)10.

10Birth order among children is also confirmed to be significantly associated with child health status. It
is reported that elder children are more favoured by their parents both intentionally and unintentionally(98-100).

12

2.3.2. Household characteristics
Parental education level: Mother’s education is confirmed to possess almost universally
positive effects on child health (96, 98, 101-117). Glewwe 1999 suggests three possible
pathways for how mother’s schooling contributes to child health and nutrition: 1) formal
education directly enhances health knowledge to future mothers; 2) literacy andnumeracy skills acquired in school help future mothers to diagnose and treat childhealth problems; 3) exposure to modern society through schooling makes women more
receptive to modern medicine (118).
Using the survey data in Ethiopia, Christiaensen and Alderman 2004 confirmed
both male and female household maximum attained education had a large and positiveeffect on child nutrition and the impact of female education was about twice as large asthat of male education (90). However, Haddad et al. 2002 using the Demographic and
Health Surveys (DHS) datasets for 16 countries, found that parental education had asignificant and positive effects on HAZ in only about one-third of cases (119)11. Davidet al. 2004 confirmed no significant impact of mother’s schooling on child nutrition in
Nicaragua (121).
Wolfe and Behrman 1987 caution that the impact of parental education may beoverestimated if important information about unobserved family background is omitted

11

Desai and Alva 1998, who also used the DHS datasets for 22 countries, obtained the similar results thatmother’s education had a significant impact on child health status only in a limited number of countries
after controlling for the socioeconomic factors and area of residence (120).

13

from the estimation (122). For instance, parental height is confirmed as a significant
determinant of child nutrition by a number of studies (86), implying the impact ofparental education might be overestimated if parental height is not included in theestimation.
Income: Positive impact of household income on child health is confirmed in numerous
empirical studies (63, 90, 92, 93, 121, 123). Most of the economic literature uses
instrumental variables for household income with a single variable or a set of variables
to control its endogenous feature. For example, Attanasio et al. 2004 use household
assets and municipality average wage as instrumental variables (123). Linnemayr et al.
2008 construct a wealth index based on principal component analysis (124) to use it as
an instrumental variable (91).
Women’s status: The relative status of women compared with men within the household
positively affects child health through women’s stronger position in decision-making inlabour supply, allocation of income, provision of child care and health-seeking
behaviours. Many empirical studies confirm that income or assets accruing to women or
under the control of women are more likely to be used for benefiting children than thoseof men (125-128).
Smith et al. 2003 examine the relationship between women’s status and childanthropometric indices in 36 developing countries from the three regions, South Asia,
Sub-Saharan Africa and Latin America and the Caribbean, during the period 1990–1998
14

by using the DHS datasets (129). They employ four types of indicators of women’s
decision-making power relative to men: 1) whether the woman works for cash income;2) the women’s age at first marriage; 3) the percent difference in the woman’s and herpartner’s age; and 4) the difference in the woman’s and her partner’s years of education.
Regression analysis shows the positive effects of women’s decision-making power onchild health, though the impact differs widely across the three regions.12

2.3.3. Community characteristics
Community factors such as the availability of local health services (95, 121, 123,
130-133)13, water and sanitation (132, 134-140) and transportation infrastructure (130)affect child health status. On the other hand, there is no clear consensus on theeffectiveness of community-based nutrition programmes which normally consist of
growthmonitoringandpromotion

and

food

supplementation

(141,

142).

Community-based growth promotion programmes had a great impact on childnutritional status in Uganda (141) and in Senegal (143), but no significant impact was
confirmed from ‘Community Nutrition Project’ in urban Senegal (144) and a milk
feeding programme in Peru (145).

12

A strongly positive effect is observed in South Asia, followed by a more moderate effect in Sub-Sahara
Africa, and only short-term effect in Latin America and the Caribbean.
13
Strauss 1990 indicates that the availability is preferable to actual utilisation of services to measure theimpact of community services (95).

15

2.3.4. Interactions between the household and community characteristics
Interactions between the household and community characteristics on child health arestudied rigorously by economists. Both substitutability (114, 130, 146) andcomplementarity (147, 148) between parental education and community factors havebeen reported. Zhao and Bishai 2004 confirmed the basic health services which ‘provide’
knowledge (access to local health infrastructure) were substitutable and the moresophisticated health services which ‘require’ knowledge (Cesarean-section) werecomplementary with the education level of the household head in China (149).

2.3.5. Heterogeneities of household and community
The effect of unobserved heterogeneities at the household and community levels (alsocalled ‘contextual effects’ (150, 151)) on child health in developing countries have been
investigated normally with a multilevel model (152, 153). Using the DHS datasets fromfive African countries, Fosto and Kuate-Defo 2005 find the community-level randomvariations are significantly different from zero (154), suggesting “neighbourhood factors
per se” affect child health status (155-157). On the other hand, Uthman 2009 confirms
no statistical significance of the community-level random effect after controlling for all
individual and socioeconomic variables (110)14.

14

The problem of those studies is that most of them use endogenous variables such as breastfeeding,calorie intake, antenatal care, and utilization of community health services as explanatory variables
without taking appropriate treatment like the instrument variables (IV) estimation.

16

2.4. Framework for the analysis
Following the theoretical framework and the empirical findings reviewed above, theframework for this paper can be drawn as Figure 4. It incorporates both observed andunobserved characteristics at the level of child, household and community. It alsoemphasises the importance of interactions between three factors.

Figure 4: Framework for the analysis
Child nutritional status(anthropometry)

Child health production
function
Health inputsChildcharacteristics
Householdcharacteristics

Communit ycharacterist ics

Biological endowment(unobserved)
Interactions

Het erogeneity ofhousehold(unobserved)

Heterogeneity ofcommunity(unobserved)

I mmediate caus es
Underlying causes
Basic causes

3. ModelTraditionally, the theoretical framework underpinning empirical analysis on thedeterminants of child malnutrition has been based on the standard Beckerian household
utility function of consumer demand to families (158, 159) and Grossman’s health17

production function (160). In this model a household is treated as a single economic
agent that maximises its welfare function defined over child health state and a vector of
consumption by family members as a whole. It is called the ‘unitary household model’.
and assumes that all income sources within the household are pooled and bargaining
powers among household members does not affect intrahousehold resource allocation.
In the unitary household model, the household head chooses a level of childhealth (h) and a consumption bundle of commodities including goods and leisure (x) tomaximise the following household utility function:ℎ, 

[1]

Health production function for the child can be expressed as:ℎ = ℎ,  ,  ,  ;  [2]where  is a vector of health inputs that household provides to the child such as
prenatal care, breastfeeding, micronutrient intake and vaccination;  is a vector ofchild characteristics such as age, gender and birth order;  is a vector of household
characteristics such as parental education, household resources and demographics; is a vector of community factors that may have an impact on child health such as theaccessibility and quality of health services; and µ is a vector of unobservablecharacteristics of the child, household and community which are assumed to be
uncorrelated with the ,  ,  , and  .
The choices of household are limited by their full income constraint:18  +   =  =  +  + !

[3]

where  is a price vector of a consumption bundle and a leisure,  is a price vectorof health input,  is household total income which contains male income ( , female
income ( ) and joint income (! ) by assuming that a household has two adults, a man(m) and a woman (f).
Several empirical studies have challenged the plausibility of the unitary
household model. Using survey data from Brazil, Thomas 1990 (161) found that
nonlabour income in the hands of women increased the probability of child survival by
20 times more than the comparable increase in male earnings. Other empirical studies
also confirmed that women preferred to spend more on child health and education,
whereas men preferred to consume luxury goods such as tobacco (162-165).
Consequently, the ‘collective household model’ was proposed in order toexplicitly incorporate a bargaining process within the household by assuming a specificstructure for parental preferences as a Pareto efficient outcome (166-168). Following
this idea, the current study applies the ‘Pareto-efficient collective household model’ toderive a reduced-form to estimate the determinants of child nutritional status. In thismodel, each adult (m=male, f=female) has a specific utility function which is definedover child health and both members’ consumptions: "ℎ, # ,  $[4]

 "ℎ, % ,  $

[5]

19

I assume there exists a weight (&) for all Pareto-efficient outcomes and therefore thehousehold’s optimisation can be expressed as:'() & "ℎ,  ,  $ + 1 − & "ℎ,  ,  $[6]subject to the household income constraint [3].
Thus, demand for child health can be derived as a function of characteristics ofchild ( ), household ( ), and community ( ), price vecors ( ,  ), householdincome ( =  +  + ! ), a weight between man and women (&), and a vector ofunobservable characteristics of the child, household and community ():ℎ = ℎ ,  ,  ,  ,  , , &; [7]

From [7] a reduced-form equation of demand for child health is written as:ℎ = +, - . + /[8]where ℎ is the health status for a child i and +′- is a vector of variables ofcharacteristics of the child, household and community, prices, income, and a weight
between man and women ( ,  ,  ,  ,  , , &,). / refers to an error term.
In this paper, a coefficient for each explanatory variable will be estimated using
simple ordinary least squares (OLS) as a benchmark. However, OLS estimates will only
be unbiased if there is no correlation between an error term and all the explanatory
variables. For example, if any explanatory variable (especially income) in the equation[8] is endogenous, OLS estimates will be biased and inconsistent. In this situation,
instrumental-variables (IV) estimation may resolve the problem. Instrumental variables
20

must be correlated with a potentially endogenous explanatory variable and uncorrelatedwith the error term. Nonetheless, IV estimates will be inconsistent if the instrumental
variables explain little of the variation in the endogenous explanatory variables (169).
For this reason, it is quite difficult to find appropriate instrumental variables in
empirical studies.
Considering the heterogeneities of households and communities which are
shown in Figure 4 as well as a sampling structure of the dataset which will be describedlater, I also apply the two-level Hierarchical Linear Model (HLM) (170, 171). Two-level
HLM can estimate random effects at both the household and community level. The
model is expressed as:ℎ!1 = +, -23 . + 4! + 41 + /!1[9]

where ℎ!1 represents health status of a child i in the household j, which belongs to thecommunity k. 4! and 41 are an unobserved household random effect and a cluster
random effect respectively.
This paper also examines interaction effects between household andcommunity factors in child nutrition. Community variables ( ) affect child healthstatus both directly and indirectly through household variables ( ). For the latterroute, the relationship between community and household variables can becomplementary or supplementary. Using the equation [7], it is shown as follows. Whena community variable 567 has an effect on child health ℎ through a household
21

variable 588 :9ℎ:;9588 9567  > 0

if

[10]

the two factors are complements; and9ℎ:;9588 9567  < 0

if

[11]

the two factors are substitutes.
Interaction effects have the important policy implications because they imply towhat extent a change of the community factors such as the improvement of community
infrastructure, water and sanitation, and local health services will bring benefits to eachof the advantaged and disadvantaged populations (in terms of income, education,
geographical area etc). For example, basic or nonspecialised local health services are
expected to act as a substitute to the poorly educated families (greater benefits to lesseducated families). On the other hand, more specialised services will be complementarywith education (greater benefits to more educated families) (149).
As to the methodological model, some studies take approach to estimate childhealth production function directly (134, 172, 173). However, most of the health inputs
into child health production such as breastfeeding, vaccination, health clinic utilisation,home-based care, micronutrients intakes are endogenous. Strauss 1990 addresses thedanger of using such endogenous variables in the OLS regression since the OLS

22

estimates in this situation lead to serious misleading interpretation of the results (95)15.
Actually, it is almost impossible for researchers to obtain all the information about thehousehold’s decision-making in child health production. Besides, it is also quite difficult
to find good instrumental variables for the endogenous health inputs variables. For thisreason, this paper does not take this approach even if the dataset does include theinformation about health inputs into child health production.

4. Data and variables
The dataset from the Lao Multiple Indicator Cluster Survey 3 (MICS3) is used for theempirical analysis.16 It includes 5,894 households comprising 33,100 members. The
main sampling domains are three regions, North, Central and South of Laos. Withineach region, 100 enumeration areas were selected with probability proportional to size.
Then, 20 households were systematically sampled from each enumeration area (83, 175,176)17.
I

examinetherelationshipbetween15

socioeconomic

factors

andAnother problem related with OLS estimation is also mentioned by Strauss 1990. Martorell et al. 1984
find that parental education has an insignificant effect on child nutritional status using a sample in Nepal(174). Strauss 1990 suggests it is because they are holding the constant variables, such as the number ofmonths breastfed and the type of food eaten, which the education variable could be expected to affect(114).
16
The MICS3 was undertaken nationwide in Laos by the Department of Statistics of Ministry ofPlanning and Investment in collaboration with the Hygiene and Prevention Department of Ministry of
Health between March and June in 2006.
17
Thus, the sample was designed to collect information of 6,000 households in total (3 @AB ×
100 B4D(EAB (( × 20ℎ4ℎGH). However, it was known in advance that one village had
only 15 households and therefore the total expected number of household was 5,995.

23

anthropometrics of children under-five 18 . First, bivariate analysis is performed toanalyse the association between the probability of becoming stunted (<-2HAZ), wasted(<-2WHZ) and underweight (<-2WAZ) and the socioeconomic characteristics. Then,
multivariate regressions are conducted to analyse Z-scores as a dependent variable.
Explanatory variables are categorised into three factors––child, household andcommunity––corresponding to the analytic framework (Figure 4).
Child variables include the age and sex of a child. Age is captured by fiveage-bracket dummies during 0-59 months. The baseline is 0-11 months of age.
Household variables comprise the information about ethnicity (by ethnic
language the household head speaks––Lao, Khmou, Hmong and ‘other’ ethnic
languages––, mother’s age, mother’s education level (‘no education’, ‘primaryeducation’ and ‘secondary education’), mother’s status within the household, andhousehold income. As a proxy for the mother’s status, a dummy variable ‘whether
mother has higher education level than father (= 1) or not (= 0)’ is used.
Since there is no information about household income in the dataset, I use thedata on household assets as a proxy for income. The dataset contains the wealth index
score which is calculated according to the principal components of household assets19.

18

Z-scores calculated based on the ‘NCHS reference’.
Household asset index is calculated based on the principal component analysis. Assets included in thecalculation are as follows: electricity, clock, radio, electric fan, mattress, black and white television,
colour television, CD/VCD player, water pump, bed, DVD player, satellite, mobile telephone, telephone,refrigerator, air conditioner, cloth washing machine, sofa, watch, bicycle, oxcart, motorbike, tractor,
Tuktuk, car or truck, engine boat, type of sanitation facility, type of cooking fuel, type of materials used
for floor, roof, and wall.

19

24

However, some of the components in the index such as water pumps, refrigerators andmotorbikes may be correlated with child health status. For this reason, I use twovariables ‘hectares of agricultural land’ per household and ‘the number of milk cows’
per household, which are not included in the calculation of the wealth index score, as aset of instrument variables for the IV estimation on the ground that these two variables
are correlated with household income and are not correlated with the error terms (/ ).
Community variables include information about the area where the household
is located (‘urban’, ‘rural with road’ and ‘rural without road’) and five variables of thecommunity characteristics. The first three: 1) households’ average time (a mean inminutes) per cluster to get water; 2) the proportion of households per cluster without alatrine; 3) the proportion of children per cluster who had diarrhoea in the last two weeks,
represent the condition of sanitation and water in the community (177, 178). The othertwo: 4) the proportion of households per cluster which own a coloured television; and 5)
the proportion of households per cluster which own a radio, are included to represent
the situation of communication infrastructure which may alter child health production
process (69). For instance, ownership of a television enables households to acquire
information about health production and leads to more efficient resource allocation (90).
Though price vectors of consumption goods, leisure and health inputs are theelements which a household consider for decision-making in child nutrition (equation[8]), I subsumed them into the unobserved community factors under the assumption that25

prices do not vary over a community (179)20. I also rule out selective migration to seek
for better health environment or more favourable prices by assumption (181).

5. Descriptive statistics
Table 1 presents the descriptive statistics for the analysis.21. The data include theinformation about 4,204 children from 2,955 households in 300 communities (clusters).
Table 1: Descriptive statistics
Variable
HAZ (Height-for-age Z-score)
WHZ (Weight-for-height Z-score)
WAZ (Weight-for-age Z-score)Male
Age 0-11 months
Age 12-23 months
Age 24-35 months
Age 36-47 months
Age 48-59 months
Ethnic language: Lao
Ethnic language: Khmou
Ethnic language: Hmong
Ethnic language: Others
Mother's age
Mother no education
Mother primary education
Mother secondary education
Wealth index score
Mother’s status compared with father
Urban
Rural with road
Rural without road
Community: Average time to get waterCommunity: Prop. of No latrine
Community: Prop. of Child diarrhoea
Community: Prop. of TV
Community: Prop. of Radio

Obs.
4033
4007
4035
4204
4136
4136
4136
4136
4136
4204
4204
4204
4204
3987
4204
4204
4204
4204
4204
4204
4204
4204
4051
4204
4204
4204
4204

Mean
-1.685-0.741-1.607
0.510
0.194
0.200
0.205
0.227
0.174
0.489
0.141
0.152
0.217
28.708
0.399
0.423
0.164
-0.257
0.066
0.168
0.543
0.289
9.460
0.618
0.124
0.321
0.429

Linearised S.E.
0.027
0.017
0.020
0.008
0.007
0.007
0.007
0.007
0.006
0.008
0.006
0.006
0.007
0.116
0.008
0.008
0.006
0.017
0.004
0.007
0.008
0.008
0.170
0.007
0.002
0.006
0.004

48.9% of the children are from households where the head speaks Lao. The

20

While the area dummy may explain a part of price fluctuation, a large portion of the variations would
be left into the unobserved community factor considering the low level of market integration in Laos
(180).
21
All of the data in descriptive summary and the estimates from the regressions are corrected for acluster survey design (170, 182).

26

proportion of the children whose mother has higher education than their father (=mother’s status) is 6.6%. More than half the children are from ‘rural with road’, while
16.8% and 28.9% of the children are from ‘urban’ and ‘rural without road’ respectively.
Table 2 summarises the prevalence of malnourished children in the sample
population according to Z-score. The prevalence of stunting (<-2HAZ), wasting(<-2WHZ), and underweight (<-2WAZ) is 39.6%, 6.6% and 36.4% respectively.
Table 2: Percentage of malnourished children by its severity
Z-scoreSeverity

below -1

Height-for-age

Weight-for-height

Weight-for-age

69.2%

38.9%

71.5%

below -2

Moderate or Severe

39.6%

6.6%

36.4%

below -3

Severe

16.0%

0.9%

9.4%

Figure 5 shows the mean of Z-scores of children under-five by 3-monthage-bracket.
Figure 5: Mean of Z-score by age 0-59 months
0.5

0
0-2

3-5

6-8

9-11 12-14 15-17 18-20 21-23 24-26 27-29 30-32 33-35 36-38 39-41 42-44 45-47 48-50 51-53 54-56 57-59

Z-score

-0.5
WHZ

-1
WAZ

-1.5

-2

-2.5

HAZ

month

27

Overall patterns of three anthropometric indices are similar to the observations
from 39 developing countries by Shrimpton et al. 2001 (97). WAZ remains above zeroof Z-score (NCHS reference) during the first 3 months of age, while HAZ starts below
zero from the first. After 3 months, both WAZ and HAZ decline steeply just before the24th month, which is considered a critical age of child growth (25, 183), stabilizing ataround -2.0 SD. After 24 months, WAZ recovers a little and keeps its score between -2.0
and -1.5 SD until the 60th month of age. HAZ fluctuates more in the range of -2.5 to -1.5
SD, taking its value of -2.3 SD at the last 3 months.
On the other hand, WAZ remains above zero during the first 6 months of age.
After 6 months, it deteriorates sharply and then mildly until around 21 months of age toabout -1.2 SD. Thereafter, it slowly keeps increasing until the 60th month of age to about-0.8 SD.

28

6. Results
6.1. Results of bivariate analysis
In Table 3, the results of bivariate analysis are presented.

Table 3: Results of bivariate analysis

Variable
Total sample
Sex of the childFemaleMale
Age of the child
0-11 months
12-23 months
23-35 months
36-47 months
48-59 months
Ethnic group
Lao
Kham
Mong
OthersMother's education
Secondary education
Primary education
No education
Mother's status
Lower than father
Higher than father
Wealth index quintile
RichestSecondMiddleFourthPoorestArea
Urban
Rural with road
Rural without road
Child diarrhoea
No diarrhoea
Diarrhoea
Household's latrine
With a latrine
No latrine

HAZ
<-2SD
(p-value)
39.6(0.713)39.9
39.3(0.000)
16.8
42.2
41.3
46.9
50.5
(0.000)
30.1
47.5
46.4
50.8
(0.000)
23.3
37.7
47.6
(0.000)39.5
39.8(0.000)
15.4
31.3
37.5
43.5
51.8
(0.000)
24.1
41.3
45.2
(0.007)38.8
45.3(0.000)
29.5
45.8

Odds
Ratio

p-value

1.000
0.975

0.713

1.000
3.617
3.487
4.372
5.062

0.000
0.000
0.000
0.000

1.000
2.100
2.010
2.398

0.000
0.000
0.000

1.000
1.996
3.001

0.000
0.000

1.000
1.010

0.940

1.000
2.503
3.304
4.234
5.905

0.000
0.000
0.000
0.000

1.000
2.214
2.597

0.000
0.000

1.000
1.309

0.007

1.000
2.015

0.000

WHZ
<-2SD
(p-value)
65.8(0.110)5.9
7.2(0.000)
4.2
12.8
7.8
3.3
4.9
(0.000)
7.0
3.4
2.4
1.0
(0.516)6.4
7.1
6.3(0.856)6.5
7.8(0.613)
6.2
6.6
7.9
6.1
6.3
(0.464)6.3
6.2
7.4(0.001)
6.1
10.2(0.003)
5.0
7.5

Odds
Ratio

p-value

1.000
1.240

0.110

1.000
3.344
1.945
0.784
1.169

0.000
0.004
0.368
0.551

1.000
0.470
0.323
1.542

0.002
0.000
0.004

1.000
1.128
0.985

0.553
0.945

1.000
1.217

0.431

1.000
1.000
1.310
0.981
1.019

0.817
0.295
0.941
0.938

1.000
0.995
1.197

0.981
0.411

1.000
1.754

0.001

1.000
1.539

0.004

Note: The p-value in a bracket is obtained by the Pearson’s chi-square test.

29

HAZ
<-2SD
(p-value)
36.4(0.596)36.8
36.0(0.000)
12.3
44.3
44.6
41.4
38.2
(0.000)
32.4
36.9
27.4
51.2
(0.000)
41.9
36.5
23.9
(0.000)36.5
35.0(0.000)
17.1
31.6
39.8
37.3
43.8
(0.000)
24.3
37.2
42.0
(0.000)35.1
45.8(0.000)
27.3
42.0

Odds
Ratio

p-value

1.000
0.964

0.596

1.000
5.686
5.750
5.047
4.423

0.000
0.000
0.000
0.000

1.000
1.219
0.786
2.187

0.052
0.027
0.000

1.000
1.824
2.289
43.4
1.000
0.937

0.643

1.000
2.237
3.214
2.888
3.776

0.000
0.000
0.000
0.000

1.000
1.842
2.257

0.000
0.000

1.000
1.562

0.000

1.000
1.929

0.000

0.000
0.000

There is no significant gender difference in the probability of becoming
malnourished. Children aged 12-59 months are much more likely to be malnourished thanthose aged 0-11 months (except for wasting among the children aged 36-47 months). The
odds of becoming stunted and underweight in children aged 12-59 months are
significantly 3.6–5.1 times and 4.4–5.8 times higher than those who are 0-11 months old
respectively.
Children from Khmou, Hmong and ‘other’ ethnic language groups are more
likely to be stunted than the children from Lao group. However, the odds of becoming
wasted in the children from Khmou and Hmong groups are significantly lower than thosefrom Lao group. Children from ‘other’ ethnic language groups are more vulnerable thanLao children for all of the indices.
Children who live in rural areas are significantly more prone to be stunted andunderweight than those who are in ‘urban’. Both recent diarrhoea incidence andownership of a latrine have a significant impact on child nutrition.

30

6.2. Results of multivariate analysis
Table 4, 5, 6 summarise the results of the multivariate analysis in three model
specifications––OLS, Two Stage Least Squared (2SLS) using instrumental variables,
and Two-level HLM (with random-intercept) ––.
The last six rows of each table show the results of a cluster random effect (41 )and a household random effect ( 4! ) in the HLM, suggesting both effects are
significantly different from zero (95% CI does not include 0) for all the anthropometricindices. Thus, we can conclude that the intercepts of 41 and 4! differ from cluster tocluster and household to household respectively.
The results of likelihood-ratio tests for all of the anthropometric indices(p=<0.000 for HAZ, WHZ and WAZ) suggest the random effect model is preferable to alinear regression with fixed intercept for 41 and 4! . Accordingly, I focus on theestimates obtained by the HLM for interpretation of the results rather than OLS or 2SLS
unless mentioned otherwise. The HLM is also used for the estimation with interaction
terms between mother’s education levels and five community variables (referred to as‘HLM2’) as well as the one without interaction terms (referred to as ‘HLM1’).

31

Table 4: Results of estimation: Height-for-age Z-score (HAZ)
Male
Age 12-23 months
Age 24-35 months
Age 36-47 months
Age 48-59 months
Ethnic group: Khmou
Ethnic group: Hmong
Ethnic group: Others
Mother's age
Mother's age^2
Mother primary edu.
Mother secondary edu.
Wealth index score
Agricultural Hectare
Number of cows
Mother's status
Rural with road
Rural without road
Community(1) Water
Community(2) No latrine
Community(3) Diarrhoea
Community(4) TV
Community(5) Radio
Mother P.E*Comm.(1)Mother S.E*Comm.(1)Mother P.E*Comm.(2)Mother S.E*Comm.(2)Mother P.E*Comm.(3)Mother S.E*Comm.(3)Mother P.E*Comm.(4)Mother S.E*Comm.(4)Mother P.E*Comm.(5)Mother S.E*Comm.(5)Constant
Observations
R-squareF-statistics
Wald χ2
Cluster (s.d.)
95% CI: Cluster
Households (s.d.)
95% CI: Households
χ2: LR test vs Linear reg.

OLS

p-value

2SLS

p-value

HLM1p-value

HLM2

0.009
-0.933-1.029-1.290-1.332-0.285-0.196-0.1830.075-0.001
0.051
0.055
0.211

0.863
0.000
0.000
0.000
0.000
0.001
0.031
0.018
0.008
0.014
0.397
0.555
0.000

0.039
-0.978-1.109-1.324-1.378-0.224-0.201-0.1780.101-0.001-0.019-0.149
0.600

0.556
0.000
0.000
0.000
0.000
0.074
0.078
0.059
0.003
0.008
0.805
0.415
0.047

0.015
-1.022-1.082-1.306-1.367-0.273-0.081-0.2250.083-0.001
0.061
0.169

0.781
0.000
0.000
0.000
0.000
0.012
0.490
0.013
0.007
0.018
0.388
0.150

0.010
-1.027-1.078-1.307-1.372-0.252-0.053-0.2340.086-0.001
0.735
0.533

0.034
0.400
0.181
0.966
0.336
0.091
0.000
0.048
0.000
0.678

0.027
0.005
-0.146
0.043
0.178
0.000
-0.216-0.2610.654
0.370-0.007-0.017-0.298-0.034-0.566-0.960-0.2860.138-0.538-0.289-2.4822632-0.0520.088
0.148-0.007-0.270-0.4880.179
0.065-1.6903824
0.1479

***
***
***
***
***
**
**
***
**

***

**
***
**

***

0.584
0.319
0.168
0.014
0.001
0.036
0.189
0.561

0.000

-0.0790.280
0.456-0.007-0.124-0.658-0.1910.178-2.1592700
0.1346
13.85

***
***
***
***
*
*
*
***
***

**

*
**
**
**

0.495
0.090
0.020
0.044
0.406
0.031
0.630
0.197

***

0.000

***

0.000

0.028
0.006
-0.1610.005
0.133-0.006-0.380-0.587
0.509
0.060

***
***
***
***
**
**
***
**

**

*
***
**
***

-1.987
2608

***

0.000

459.260.159[0.0700.475[0.357
18.2

***

0.000

-

0.360]***

0.632]
0.000

473.920.151[0.0620.479[0.36118.01

p-value
***
***
***
***
**
**
***
**
***

**

***

0.039
0.499
0.231
0.731
0.201
0.973
0.172
0.539
0.014
0.113
0.317
0.125
0.128
0.912
0.334
0.342
0.328
0.717
0.067
0.539
0.000

***

0.000

-

0.369]***

0.634]
0.000

**

*

Note: * indicates significance at 10% level, ** at 5% level and *** at 1% level of confidence.
For the 2SLS, ‘hectares of agricultural land’ and ‘the number of cows’ are used as instruments for the wealth index.
For the HLM, method of estimation is a maximum likelihood.

32

0.859
0.000
0.000
0.000
0.000
0.022
0.656
0.010
0.005
0.015
0.003
0.174

Table 5: Results of estimation: Weight-for-height Z-score (WHZ)
Male
Age 12-23 months
Age 24-35 months
Age 36-47 months
Age 48-59 months
Ethnic group: Khmou
Ethnic group: Hmong
Ethnic group: Others
Mother's age
Mother's age^2
Mother primary edu.
Mother secondary edu.
Wealth index score
Agricultural Hectare
Number of cows
Mother's status
Rural with road
Rural without road
Community(1) Water
Community(2) No latrine
Community(3) Diarrhoea
Community(4) TV
Community(5) Radio
Mother P.E*Comm.(1)Mother S.E*Comm.(1)Mother P.E*Comm.(2)Mother S.E*Comm.(2)Mother P.E*Comm.(3)Mother S.E*Comm.(3)Mother P.E*Comm.(4)Mother S.E*Comm.(4)Mother P.E*Comm.(5)Mother S.E*Comm.(5)Constant
Observations
R-squareF-statistics
Wald χ2
Cluster (s.d.)
95% CI: Cluster
Households (s.d.)
95% CI: Households
χ2: LR test vs Linear reg.

OLS

p-value

2SLS

p-value

HLM1p-value

HLM2-0.029-0.896-0.744-0.626-0.6360.254
0.597-0.085-0.0240.000-0.011
0.119
0.022

0.441
0.000
0.000
0.000
0.000
0.000
0.000
0.274
0.574
0.620
0.449
0.020
0.600

-0.025-0.945-0.788-0.615-0.6160.331
0.647-0.127-0.0340.001-0.024
0.100
0.169

0.519
0.000
0.000
0.000
0.000
0.000
0.000
0.031
0.112
0.142
0.590
0.371
0.333

-0.045-0.949-0.795-0.609-0.5760.296
0.578-0.164-0.0400.001-0.016
0.102

0.183
0.000
0.000
0.000
0.000
0.000
0.000
0.008
0.034
0.050
0.706
0.163

-0.043-0.943-0.792-0.610-0.5740.289
0.547-0.169-0.035
0.001
0.162
0.010

0.651
0.133
0.892
0.572
0.316
0.547
0.029
0.432
0.433
0.214

0.004
0.006
-0.0010.063
0.104-0.007-0.025-0.337-0.056
0.279
0.006
0.014
-0.154-0.3880.242
0.629-0.075-0.021-0.362
0.186
0.221
2608

-0.0230.101
0.091-0.003-0.071-0.332-0.019
0.160

0.093
3826
0.1792

***
***
***
***
***
***

**

*
*
*
**

0.287
0.092
0.208
0.073
0.150
0.059
0.790
0.044

0.553

-0.0370.088
0.129-0.002-0.061-0.191-0.231
0.151

0.319
2676
0.2029
24.90

***
***
***
***
***
***
**

*

0.627
0.404
0.274
0.394
0.480
0.305
0.342
0.083

0.349

***

0.004
0.007
-0.0100.049
0.100-0.002-0.159-0.173-0.080
0.133

***
***
***
***
***
***
***
**
*

**

0.391
2608

0.202

p-value
***
***
***
***
***
***
***
*
*

*

*
*
**

*

0.635
0.146
0.992
0.465
0.294
0.074
0.819
0.259
0.748
0.080
0.140
0.053
0.216
0.046
0.511
0.323
0.680
0.931
0.050
0.529
0.504

0.000
552.040.226[0.1750.238[0.143
39.64

***

0.000

-

0.290]***

0.395]
0.000

575.070.216[0.1660.237[0.142
35.61

***

0.000

-

0.281]***

0.395]
0.000

Note: * indicates significance at 10% level, ** at 5% level and *** at 1% level of confidence.
For the 2SLS, ‘hectares of agricultural land’ and ‘the number of cows’ are used as instruments for the wealth index.
For the HLM, method of estimation is a maximum likelihood.

33

0.199
0.000
0.000
0.000
0.000
0.000
0.000
0.006
0.065
0.091
0.291
0.967

Table 6: Results of estimation: Weight-for-age Z-score (WAZ)

Male
Age 12-23 months
Age 24-35 months
Age 36-47 months
Age 48-59 months
Ethnic group: Khmou
Ethnic group: Hmong
Ethnic group: Others
Mother's age
Mother's age^2
Mother primary edu.
Mother secondary edu.
Wealth index score
Agricultural Hectare
Number of cows
Mother's status
Rural with road
Rural without road
Community(1) Water
Community(2) No latrine
Community(3) Diarrhoea
Community(4) TV
Community(5) Radio
Mother P.E*Comm.(1)Mother S.E*Comm.(1)Mother P.E*Comm.(2)Mother S.E*Comm.(2)Mother P.E*Comm.(3)Mother S.E*Comm.(3)Mother P.E*Comm.(4)Mother S.E*Comm.(4)Mother P.E*Comm.(5)Mother S.E*Comm.(5)Constant
Observations
R-squareF-statistics
Wald χ2
Cluster (s.d.)
95% CI: Cluster
Households (s.d.)
95% CI: Households
χ2: LR test vs Linear reg.

OLS

p-value

2SLS

p-value

HLM1p-value

HLM2

0.009
-1.132-1.203-1.160-1.1520.030
0.355-0.143
0.026
0.000
0.015
0.110
0.144

0.811
0.000
0.000
0.000
0.000
0.631
0.000
0.009
0.198
0.237
0.735
0.117
0.000

0.024
-1.224-1.309-1.193-1.1880.108
0.401-0.1710.037
0.000-0.028
0.000
0.433

0.609
0.000
0.000
0.000
0.000
0.221
0.000
0.010
0.133
0.199
0.607
0.998
0.050

0.004
-1.228-1.287-1.151-1.1270.060
0.422-0.218
0.021
0.000
0.015
0.157

0.912
0.000
0.000
0.000
0.000
0.490
0.000
0.002
0.347
0.459
0.767
0.071

0.003
-1.227-1.283-1.153-1.1280.061
0.413-0.231
0.026
0.000
0.524
0.227

0.080
0.112
0.168
0.901
0.203
0.048
0.000
0.029
0.031
0.188

0.016
0.008
-0.1050.048
0.175-0.005-0.172-0.4560.367
0.447-0.002-0.001-0.215-0.244-0.1920.051-0.2710.122-0.569-0.046-1.4052632-0.0640.124
0.178-0.007-0.192-0.5830.108
0.182-1.0823826
0.2206

***
***
***
***
***
***

***

**
**
***
***
***
**

***

0.367
0.043
0.018
0.001
0.001
0.001
0.258
0.026

0.001

-0.0990.198
0.358-0.006-0.112-0.622-0.2120.247-1.2132700
0.2301
26.11

***
***
***
***
***
**

*

*
**
**
***
**

0.262
0.092
0.011
0.013
0.292
0.008
0.468
0.012

***

0.001

***

0.000

0.017
0.008
-0.121
0.012
0.143-0.006-0.321-0.536
0.252
0.158

***
***
***
***
***
***

*
*

**
***
**
**

-1.024
2632

***

0.005

784.490.221[0.1640.390[0.308
45.97

***

0.000

-

0.297]***

0.494]
0.000

803.200.216[0.1580.390[0.30743.84

p-value
***
***
***
***
***
***

***

*

***

0.083
0.142
0.235
0.632
0.120
0.278
0.168
0.183
0.070
0.015
0.742
0.935
0.143
0.287
0.660
0.947
0.209
0.666
0.009
0.896
0.000

***

0.000

-

0.293]***

0.494]
0.000

*
**

***

Note: * indicates significance at 10% level, ** at 5% level and *** at 1% level of confidence.
For the 2SLS, ‘hectares of agricultural land’ and ‘the number of cows’ are used as instruments for the wealth index.
For the HLM, method of estimation is a maximum likelihood.

34

0.941
0.000
0.000
0.000
0.000
0.486
0.000
0.001
0.252
0.354
0.004
0.437

6.2.1. Child characteristics
There is no evidence that male children are more malnourished than females for all theanthropometric indices. Child’s age, which is expressed by four dichotomous variables,
is found to be quite significantly associated with child nutritional status. Children aged
more than or equal to 12 months of age are more stunted, wasted and underweight thanthose under 12 months at the less than 1% significance level. WHZ and WAZ decrease
until 23 months and 35 months of age respectively, but both indices level off thereafter.
However, HAZ keeps falling throughout all the age-bracket until 60th months of age.

6.2.2. Household characteristics
There are striking differences between the ethnic groups. As to HAZ and WHZ, thechildren from Khmou and ‘other’ language groups are more stunted than those from Laogroup (baseline group) at the less than 5% significance level, whereas the children fromKhmou and Hmong groups are less wasted than those from Lao group at the less than
1% significance level. The children from ‘other’ language groups are more wasted thanethnic Lao children (p=0.008). With regard to WAZ, the children from Hmong group areless malnourished (p<0.001), but the children from ‘other’ language groups are more
underweight than ethnic Lao children (p=0.001). It is important to keep in mind that thechildren from Hmong groups have equally or better nutritional status than those from
Lao group, whereas the children from ‘other’ language groups are more malnourished35

than those from Lao group for all the indices even after controlling for socioeconomic
factors for which this population is disadvantageous22.
The effect of mother’s education seems very limited in contrast to numerous
past studies. Mother’s secondary education has a positive impact on WAZ, albeit with asmall statistical significance (p=0.071). Mother’s age has a positive and diminishing
impact on HAZ at the less than 1% significance level, whereas it affects a negative
impact on WHZ (p=0.034). Mother’s status within the household does not exhibit any
significant effect on child nutrition.
In the OLS model, the wealth index score has a significant and positive impact
on HAZ (p<0.001) and WAZ (p<0.001). Nonetheless, its significance becomes
weakened in the 2SLS (p=0.047 and p=0.050 respectively) when the wealth index isinstrumented with ‘hectare of agricultural land’ and ‘the number of cows’. The F-tests
for the significance of the instruments give p<0.001 for all the anthropometrics,
indicating the proposed instruments have a predictive power to explain the wealth index
score23. Concerning WHZ, the asset index does not affect any significant impact.

6.2.3. Community characteristics
There is no significant association between the area dummy and anthropometric indices.
22

Andersson et al. confirm that poverty is concentrated and more severe among ethnic minority groups
in Laos especially due to the limited access to productive resources (184).
23
DWH (Darbin-Wu-Hausman) test for endogeneity is not available for cluster-weighted sample.
However, DWH test for a non-weighted sample in the same dataset shows that p-value from the DWH
test is 0.2375, 0.2752 and 0.1379 for HAZ, WHZ and WAZ respectively. Therefore, we cannot reject theexogeneity of the wealth index.

36

Households’ average time per cluster to get water has a weakly significant and negative
impact on HAZ (p=0.091) and WAZ (p=0.048). Low latrine coverage has a strongly
significant and negative impact on HAZ (p<0.001) and WAZ (p<0.001), as well as on
WHZ (p=0.029). Recent child diarrhoea exhibits a negative impact on HAZ and WAZ atthe less than 5% significance, whereas it has no significant impact on WHZ.
The television coverage has a significant and positive effect on HAZ (p<0.001)
and WAZ (p=0.031), but not on WHZ. On the other hand, the radio coverage is not
significantly associated with any of the nutrition indices.

6.2.4. Interaction of mother’s education and community factors
A column of HLM2 in Table 4, 5, 6 presents the results of interaction terms between
mother’s education levels and community factors. With regard to WHZ, the mother’s
secondary education is confirmed to be weakly substitutable with the household’s
average time per cluster to get water for WHZ (p=0.053) and also to be complementarywith the latrine coverage (p=0.046). For all the child anthropometrics, the mother’s
primary education seems to have a substitutable relationship with the radio coverage(p=0.067 for HAZ, p=0.050 for WAZ and p=0.009 for WAZ).

6.2.5. Unobserved heterogeneities at the household and community levels
As explained above, the results of the HLM suggest that the unobserved heterogeneities
at both the household and community levels are significantly associated with all the37child anthropometric indices.

7. Discussion
7.1. Main findings
This paper has elucidated the relationship between socioeconomic factors and childnutritional status in Lao PDR using cross-sectional survey data. Main findings are
summarised as follows.
First, the results have identified the socioeconomic factors which affect an
impact on children’s nutritional status: ethnic groups, mother’s age, mother’s educationlevels, household assets, water and sanitation, and communication infrastructure.
Importantly, the factors which exert an effect on WHZ are quite different from those of
HAZ and WAZ. For instance, only one community variable (low latrine coverage)
exhibits a significant effect on WHZ, whereas four of the five community variables areimportantly associated with HAZ and WAZ. This result is consistent with theobservations by Shrimpton et al. 2001 (97).
Second, the paper has identified the patterns of child growth faltering by age.
The multivariate analysis shows the age of child is directly associated with theirnutritional status––children aged more than 12 months are significantly more
malnourished than those under 12 months––. Noticeably, HAZ deteriorates even after 2
38years of age, whilst WHZ and WAZ stabilise earlier than that age.
Finally, it has been confirmed that mother’s status within the household(measured by the mother’s relative educational achievement against father) has no
significant impact on child nutritional status, suggesting that the ‘collective household
model’ is not supported by the data. On the other hand, the paper has found unobserved
heterogeneities of both the household and community are significantly associated withchild health status, implying the existence of ‘neighbourhood effect’ in the sample
population.

7.2. Policy implications
There is a global recognition of the efficacy of a number of nutrition interventions indeveloping countries such as breastfeeding, micronutrient supplementation and certain
types of health care services (17, 18, 185-187). Nonetheless, these interventions are
generally targeted for the elimination of immediate causes of child malnutrition. The
empirical results suggest that socioeconomic factors are importantly associated withchild nutrition, suggesting that such shorter-term nutrition interventions might not be
sufficient but should be supplemented by broad social policies to tackle the underlying
and basic causes to achieve a further reduction in child malnutrition (Table 7). From
this point of view, the following recommendations are suggested.

39

Table 7: Interventions to better nutrition

Supply-side
incentives

Demand-side
incentives

Demand-side
behaviourchangeTo: Immediate causes(short-term) Community-based nutrition and healthservices Facility-based nutrition and healthservices Micronutrient supplements
 Targeted food aid
 Conditional cash transfers
 Microcredit cum nutrition education
 Food supplementation
 Food stamps
 Maternal nutrition, knowledge, andcare-seeking during pregnancy
 Hygiene education

To: Basic and underlying causes(long-term) Safe water and sanitation
 Primary health services and infectious
disease control
 Food and agricultural policies
 Food industry development Fruit and vegetable production
 Economic development (income creation forthe poor) Participatory programme Food price policy Employment creation
 Improvement of women’s status
 Reducing women’s workload
 Increasing women’s education

Modified from World Bank (2006) (18)

Recommendation 1: Institutionalise nutrition into socioeconomic development
The results of the study suggest child malnutrition is strongly associated with othersocial and economic sectors. For instance, the results show that the poor conditions ofwater and sanitations have a negative impact on both acute and chronic child nutrition.
On the other hand, water and sanitation are also known to have an effect on a country’s
economic performances. According to the recent report by WSP 2009, inferior
sanitation conditions cause serious damage to Lao economy.24 Given the low sanitation
coverage in Laos compared with other countries in the region, 25 more ambitious
investment into the water and sanitation sector should be considered.
The current Lao National Socio-Economic Development Plan (NSEDP) directs
24

According to the WSP 2009, Lao PDR made a loss of USD 193 million (approximately 5.6% of GDP)due to poor sanitation and hygiene in 2006 (188).
25
Sanitation coverage was 48% in 2005, which was much lower than the regional average for Southeast
Asian countries of 67%. There is also a huge disparity between urban (84%) and rural (32%) (189).

40

more enhanced growth by strengthening a link between economic development andsocial development. 26 Considering its multiple causes and consequences, childmalnutrition is should be treated in a broader socioeconomic context rather than being
handled independently. Evidence suggests that multisectoral interventions lead to great
health gains (191-194). For the above reasons, policymakers should institutionalise
nutrition interventions into a national socioeconomic development framework (policies
and programmes27) so that they could promote multisectoral approach to improve childmalnutrition more effectively.

Recommendation 2: Targeting resources to the most disadvantaged populations
The empirical analysis reveals the socioeconomic background of the most malnourishedchildren in Laos. Considering the limited financial and human resources for healthspending in Laos (196), a priority of the nutrition interventions should be given to themost vulnerable children. Policymakers should deliberate a plan on how to deliver
necessary resources to the targeted populations by applying the basic principle oftargeting as well as learning from good practices in other countries such as PROGRESA
in Mexico (197).
For example, a strong social network in rural Lao villages28 will be helpful to
26

The six NSEDP (2006-2010) articulates the following directions to achieve higher socioeconomic
development outcomes: 1) turn from under-development to fast and stable development; 2) increase
competitiveness; 3) strengthen links between economic development and social development; and 4)
accelerate the building of a comprehensive socio-economic infrastructure (190).
27
Such as the Village Development Funds and the Poverty Reduction Fund (195).
28
Patcharanarumol 2009 states that loans can commonly be made without credit guarantees or interest

41

select the right beneficiaries by community members (individual assessment).29 The
information about prevalence of child malnutrition by ethnic group or geographic area
could help policymakers to select a districts for, for example, NGO activities such as theDistrict-based Comprehensive Primary Health Care programme in Sayaboury province(201) (categorical/geographic targeting). Policymakers can differentiate the quality,
quantity and price of goods for child health production such as Oral Rehydration
Therapy (ORT) according to the needs of the households or communities(self-selection).

Recommendation 3: Design pro-poor nutrition interventions
The empirical results show that the mother’s secondary education serves as acomplement for the low latrine coverage in determining weight-for-height of children. It
suggests that social investment into toilet facilities without consideration of theinteraction effects would be more benefit to the children from better educated families
rather than disadvantaged children. In such an occasion, Behaviour Change
Communication (BCC) activities about sanitation and hygiene should be also provided
to poorly educated mothers in order to make the most of the investment and to prevent
unequal child health gains. As such, in implementing nutrition policies, policymakers
should scrutinise the socioeconomic background of targeted children to anticipate

for meeting health expenditure within the village in Lao unlike in Vietnam and Cambodia (198).
29
Categorisation of the targeting methods (‘individual assessment’, ‘categorical/geographical targeting’and ‘self-selection’) is based on Hanson 2008 (199) and Conning 2001 (200).

42

possible interaction effects with community factors so that a pro-poor resource
allocation will be realised.

7.3. Study limitations and suggestions for future researchSome caution should be taken in interpreting the empirical results. First, variables
representing child biological endowments such as parental height or weight are notincluded in the estimations,30 suggesting the impact of parental education would beoverestimated. However, the empirical results show that the mother’s education affects
only a limited impact on child nutritional status. It is not clear whether this phenomenon
is caused by a real effect of mother’s schooling on child health or by the other reasons.
Further investigation is required on biological endowment and the effects of schooling.
Second, prices of commodities, leisure and health inputs are subsumed into theunobserved community factors under the assumption that they do not vary over acommunity. However, it is desirable to see actual variations in these prices by using theother source of data in order to check whether the assumption is plausible or not.
Finally, the direction of causation between socioeconomic factors and childhealth are ambiguous since this paper uses only cross-sectional data. Utilisation of
longitudinal datasets or randomised trials would be preferable to scrutinise the actual
causalities.
30

Although the MICS3 dataset includes the information about mother’s height and weight, I do not usethat information because response rates are quite low.

43

8. Conclusion
Improving children’s nutritional status brings multiple and long-term benefits. Given thecomplexities of the mechanism of child malnutrition, this study suggests a framework
which incorporates heterogeneities at both the household and community levels. The
empirical results have confirmed that socioeconomic factors such as mother’s education,
ethnicity, household assets and community environment have a significant impact onchild nutritional status. It is verified that children aged more than or equal to 12 months
are more malnourished than those below 12 months of age. The study has also revealed
that unobserved heterogeneities of both household and community are associated withchild health status.
Following the empirical results, policy recommendations are suggested asfollows. First, policymakers should institutionalise nutrition into a socioeconomic
development framework considering the multiple causes and consequences of childmalnutrition. Second, they should target resources to the disadvantaged populations
such as ethnic minority groups, impoverished households and communities with poor
sanitation environment. Third, social policies and nutrition interventions should bedesigned carefully by scrutinising the socioeconomic background of children in order toachieve a pro-poor resource allocation.

44

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