DP2014-17

Nutritional Status of Children,
Food Consumption Diversity
and Ethnicity in Lao PDR*Sam uel Kobina ANNIM
Katsushi S. IMAI
March 29, 2014

* The Discussion Papers are a series of research papers in their draft form, circulated to encourage
discussion and comment. Citation and use of such a paper should take account of its provisional character.
In some cases, a written consent of the author may be required.

NUTRITIONAL STATUS OF CHILDREN, FOOD
CONSUMPTION DIVERSITY AND ETHNICITY IN LAO PDR1Samuel Kobina Annim
Department of Economics, University of Cape Coast, Ghana
Katsushi S. Imai *Economics, School of Social Sciences, University of Manchester, UK& RIEB, Kobe University, Japan
26th March 2014

Abstract
This study examines the effect of consumption of diversified diets and cultural practices on
the nutritional status of children less than five years. The primary hypothesis of the study isthat rearing of poultry, sheep and goats enable households to have access to diversified fooditems, which in turn increases the nutritional status of children in the household. Ordinary
Least Squares and Instrumental Variable estimations techniques are employed based on asample of over 10,000 children less than five years old from the 2011 Lao Social Indicator
Survey. The main finding is that children in households that rear livestock consume
diversified diets and that in turn leads to higher nutritional status. Both positive and negative
statistically significant signs are observed for the prevalence of malnutrition across different
ethnic groups in Lao PDR. Thus a one-size fit all intervention for malnutrition will havechallenges. From a policy perspective, there should be a campaign for the consumption ofdiversified foods rather than a single or a couple of food items. To ensure the consumption ofdiversified food items, rearing of livestock has to be promoted through alternative options
including educational campaigns.
Keywords: Diversified Diet, Livestock, Ethnicity, Height-for-age, Weight-for-Age andWeight-for-Height, Lao PDR.
JEL Codes: I12; I18 and Q18
Corresponding Author
Katsushi S. Imai (Dr.)
Economics, School of Social Sciences, University of Manchester, Arthur Lewis Building, Oxford Road,
Manchester M13 9PL, UK; Phone: +44-(0)161-275-4827 ; Fax: +44-(0)161-275-4928 ; E-mail: e-mail:subhasish.dey@manchester.ac.uk

Introduction
1

This study has been funded by IFAD (International Fund for Agricultural Development). We are grateful to
Stefania Dina for her support and guidance throughout the study.

1Adequate nutrition is essential for child’s intellectual and physical development. Therefore
ensuring that infants and children have adequate nutrition is an important step towards a healthy
populace. This translates into reduced public expenditure on health curative related issues andalso promotes economic growth. In response, awareness about child malnutrition2 and taking
pragmatic steps to reduce its incidence via strategies such as food security has heightened. The
forgoing is supported by the observation that target 1C of the first Millennium Development
Goals (Eradicate Extreme Poverty and Hunger by 2015) identifies prevalence of underweightchildren under-five years of age and proportion of population below minimum level of dietaryenergy consumption as the targets. To this end various nations, especially in developing
countries have instituted programmes and action plans to promote better nutritional status fordifferent age groups (infants, children and adults). In spite of these attempts nearly 870 million
people globally, were malnourished between 2010 and 2012 (Food and Agriculture Organization(FAO), 2012). Not surprisingly, about 98 percent of these people live in developing countries.
Like all other poverty indicators, the vulnerable and children are most affected by malnutrition.
About half of the estimated 10.9 million child deaths worldwide have been attributed to poor
nutrition (Can and Hunger Notes, 2013). Also, malnutrition has been attributed to a number of
diseases because it weakens the immune system to fight bacteria. Available evidence suggeststhat malnutrition as an underlying contributing factor to a number of diseases and child death isas follows: diarrhea (61%); malaria (57%); measles (52%); pneumonia (45%) and child death(53%) (Black, Morris, & Bryce, 2003; Bryce, Boschi-Pinto, Shibuya, Black, & WHO Child
Health Epidemiology Reference Group, 2005).
The proportion of children stunted (<-2SD of height-for-age z-scores), wasted (<-2SD of weightfor-height z-scores), and underweight (<-2SD of weight-for-age z-scores), have been used asindicators for measuring Target 1C of MDG1 (Annim, Awusabo-Asare, Amo-Adjei, & ICF
International, 2013). Current global estimates put these indicators at 106 million stunted, 101
million underweight and 52 million wasted. An approximated 70 to 90 percent of the global
incidence of malnutrition is in Africa and Asia. The fact that these two regions have high rates ofchild malnutrition is not surprising since they also have the highest poverty rates in world.
Notwithstanding the fact that Southern Asia has achieved progress in reducing malnutrition inrecent years, the region still has the highest proportion of underweight children (31%) (MDG
Report, 2013). In South East Asia and the Pacific, the prevalence at the aggregate masks thewide differentials at the individual country level. Figure 1 presents prevalence rates of stunting,
wasting and underweight of children less than five years for countries in South East Asia and thePacific for 2010. However, in India and Thailand the available data is for 2005/06 and 2011 forVietnam. Comparing countries with data on malnutrition for 2010-11 that is Lao PDR, Bhutan,
Vietnam and China, Lao PDR has the highest prevalence for all three anthropometric indicators.
In deed for underweight and wasting the rates in Lao PDR is twice that of Bhutan and China.
Prevalence rates of Lao PDR is based on the recent Lao PDR Social Indicator Survey (20112

In the paper, malnutrition is used to refer to children with <-2SD for height-for-age, weight-for-age and weightfor-height. Proportion of overweight is negligible in Lao PDR (about 2%).

2

2012) which reports 27 percent of children under age five are moderately underweight and sevenpercent are severely underweight; 44 percent are also moderately stunted and 19 percent are
severely stunted and six percent are reported as moderately wasted with one percent severely
wasted. Although the malnutrition prevalence rates reported for India relates to a 2005/06 survey
which makes comparison with 2010 statistics a bit restrictive, the high prevalence which is about
the same for Lao PDR in the same year (Figure 2) is worth highlighting. This is because thecomparable prevalence rates will constitute a good base for benchmarking differences in policies
in the two countries that might have contributed to changes in malnutrition prevalence rates.

Figure 1: Prevalence of Malnutrition by Countries in South East Asia and The Pacific in 2010
Source: www.childinfo.org

Since 2000 different surveys have captured information on the prevalence of child malnutritionin Lao PDR. Use of such surveys should take into consideration the differences in sampling sincethough nationally representative; some are skewed towards the rural areas whereas others haveboth rural and urban focus. Figure 2 presents malnutrition in Lao PDR from six national surveys
namely; National Health Survey (NHS), (2000); Multiple Indicator Cluster Survey (MICS),(2006); World Food Programme (WFP), (2006); Lao PDR Social Indicator Cluster Survey(LSIS), (2011) and Risk and Vulnerability Survey (RVS), (2013). Two of the six surveys (WFP
and RVS) although had a national coverage in terms of provincial and agro-ecological zone,
representation targeted only rural households.

3

Prevalence of malnutrition

60.0
50.0
40.0

48.0

50.0

31.0

31.0

41.0
40.0

30.0

44.0

46.0

27.0

25.3

Stunting

20.0

Wasting

15.0
10.0

Underweight

7.0

7.5

6.0

5.4

MICS 2006

WFP 2006

LSIS 2011

RVS 2012

0.0
NHS 2000

Nationally Representative Surveys
Figure 2: Trends of Malnutrition in Lao PDR based on different Nationally Representative Survey for the
Period 2000 to 2012

Of the three malnutrition indicators, wasting has declined more over the period 2000 to 2012
irrespective of the sample target of the survey that is either rural or both rural and urban. Whileunderweight has also declined over the same period the percentage change is about 15 with therecent survey (RVS) showing that underweight in Lao PDR is 25.3. Stunting rates over theperiod 2000 to 2012, have fluctuated both for surveys with rural and urban orientation as well ascomparison across surveys with different sample (rural and urban) orientation. Comparing the
NHS 2000 with LSIS 2011, both rural and urban orientation, children too short for their heights(stunted) have increased by three percentage points. On the other hand, comparing height-for-age
for children less than five years using the two surveys with a sample inclination towards rural
households (WFP 2006 and RVS 2012), stunting in Lao PDR for rural households has declined
by four percentage points.
The minimal and varied progress made towards reducing malnutrition in Lao PDR deepens
concern on the consequences of malnutrition. Figures 3 and 4 corroborate the positive
association between under-5 mortality and child malnutrition for different regions and among
ethnic groups in Lao PDR.

4

104

51

47

50
38

40
26

30

73

120

101

100
80

35

60

23

20

40

10

8

5

5

20

0

CHILD MORTALITY RATEPREVALENCE OF MALNUTRITION

60

0
North

CentralSouthREGIONS IN LAO PDR
Underweight
Wasting

Stunting

Child Mortality

70

PREVALENCE OF
MALNUTRITION60

61 160

61

56

50
108

40
30

33

76

43
37
74

26

21

20
10

6

13
7

2

0
Lao-Tai

Mon-khmer

180
160
140
120
100
80
60
40
20
0

CHILD MORTALITY
RATE

Figure 3. Regional Prevalence of Malnutrition and Under-5 Mortality in Lao PDR in 2011
Source: LSIS Survey Data (2011)

Hmong-Mien Chinese-Tibetan

ETHNIC GROUPS IN LAO PDR
Stunting

Wasting

UnderweightChild Mortality

Figure 4: Prevalence of Malnutrition among Ethnic Groups and Under-5 Mortality in Lao PDR in 2011
Source: LSIS Survey Data 2011

The Government of Lao PDR (GoL) has over the years implemented programmes and policies toreduce the incidence of poverty and improve nutrition. Most recent of such policies are the“Right to Adequate Food” which the GOL ratified the International Covenant on Economic,
Social and Cultural Rights (ICESCR) (50) in 2007 and the “National Nutrition Policy” (NNP)
formulated in 2008. The NNP identified priority areas to tackle the malnutrition menace and as
well set targets to be achieved by 2015 and 2020. Prevalence rates presented in Figure 2
indicates a slow progress in achieving all three anthropometric targets set in 2015 that is stunting(34%), wasting (4%) and underweight (22%). This raises a number of questions including thefollowing: were the targets overambitious; were there any signals of poorly targeting the ‘right’group of malnourished children and were the targets divergent in a manner that inhibited the5optimal harnessing of the expected benefits. The latter justifies the recommendation of aconvergence approach in addressing the malnutrition menace in Lao PDR (United Nations,2013). The Multisectoral Food and Nutrition Security (FNS) Action Plan designed for the period
2014 – 2020 primarily, aims at accelerating the progress in reducing undernutrition via theimplementation of both nutrition-sensitive and nutrition-specific interventions. In this regard,
agriculture, education, health and water, sanitation and hygiene (WASH) related interventions
would be implemented in the next seven years. Examining the conditional effect of each of thesefactors on malnutrition, as per this paper’s orientation, is therefore imperative and timely.
From a theoretical perspective, manifestation of malnutrition (stunting, wasting andunderweight) is underpinned by three major constructs: structural (residence and geographic),
underlying household/family (sanitation, parental education, wealth, childcare practices),
immediate (diseases, health promotion and prevention and food security – times child was fed,
child characteristics e.g. age, sex birth order etc.) (UNICEF, 1990). Thus the extant literature
identifies food intake as one of the proximate (immediate) determinants of child’s nutritional
status. Though this might be suggestive that the relationship between food security andnutritional status is well known, there exists a knowledge gap on the transmission mechanism
given the comprehensive definition of food security. Food security from the extant literature
have been defined to include at least four diverse components namely; food quantity, quality,
diversity and sustainability. Returns of a policy intervention aimed at ensuring food security will
largely depend on which component of food security is being pursued. For instance, a policy onfood security from a supply (production) perspective will address the availability of food concern
but not likely to translate into impact positively on nutritional status. The impact on nutritional
status is determined by the nutrients in food consumption and a variety of food items consumed.
The relationship between food security and nutritional status is also marred by differences in unitof observation and/or analysis. That is in examining the nutritional status of children in ahousehold, observation/measurement of food security could be at different levels namely;
children, mothers and household. Choice of any of these three will impact on the nutritional
status of the children in the household however, the transmission mechanism for the impact
would be different and also might take different time periods. For instance, mothers who arefood secured are likely to produce nutritious breast milk for their children which will translate
into better nutritional status of the children. However, for children more than two years of age
other direct consumption of other semi-solid food items will engender a much more positive
nutritional status if complemented with the nutritious breast milk. These complexities have
paved way for this exercise to test the following hypotheses: consumption of individual fooditems namely; infant formula, milk, yoghurt or semi-solid food have positive effect of thenutritional status of children and consumption of a diversified basket of food items yields a
positive effect on the nutritional status of children. The importance of dietary diversity in thechild health production function is underscored in Hooshmand and Udipi (2013) who found that
dietary diversity scores were high among children who had normal weight and overweight andlowest among children who were underweight.
6

As indicated earlier, the UNICEF, (1990) conceptual framework for the manifestation ofnutritional identifies household/family conditions as the underlying factors. In this regard, theissue of care is illuminated. Since care is intangible, several factors have been used as proxies tomeasure the degree of care that a child is likely to get from the household/family. This exercise
extends its orientation to examine factors that are likely to influence the care that a child would
receive as a result of mother’s educational status and ethnic group of the household.
In Lao PDR, poor maternal education has been cited as one of the causes of malnutrition. The
LSIS (2012) estimate that about 69 per cent of young women (age 15 – 24) are literate compared
to 77 % young men, with variation across regions. Studies that have tried to link education tomalnutrition try to identify pathways through which the education effect is realized. A review ofthe literature show four broad pathways: socioeconomic status; women’s empowerment andautonomy; health knowledge and attitudes; and health and reproductive behavior (see Makoka &ICF International, 2013). Thus empirical works in this area have modeled the effect of maternal
education in the child health production function following these broad themes.
The main argument of the models on women’s bargaining power is that through education
women are able to participate in the decision making process which ultimately affects childhealth outcomes. Thus educational attainments allow women to exert higher control over healthchoices that affect them as well as their children (Hobcraft, 1993). In the view of Frost, Forste,
and Haas, (2005) maternal education creates awareness about child health with regards to causes,
prevention and treatment of diseases. Also it does promotes awareness on the importance of
immunization (Ruel, habicht, Pinstrup-Andersen, & Grohn, 1992) and causes a behavioral shift
from just accepting child health outcomes as given towards the implementation of simple healthknowledge. Studies that have investigated the relationship between child health outcome andmaternal education have found a positive impact of education on child health. Makoka (2013)Show that the threshold necessary for women’s education in significant reduction in child healthranges from at least five years of schooling in Tanzania to eight years in Zimbabwe and nine
years in Malawi. In their study in India, Imai et al. (Imai, Annim, Gaiha, & Kulkarni, 2012)concluded empowering women through education has positive impact on the weight-for-age andweight-for-height of children. An earlier study by Phimmasone, Douangpoutha, Fauveau, andPholsena, (1996) shows that Laotian women who had complete primary education were lesslikely to have children who are stunted or wasting.
One other factor that greatly influences child health outcomes in developing countries is culturalbeliefs. Cultural beliefs and practices can have both positive and negative impact on child health.
Some societies have strict codes on diet for pregnant and infants that prohibits the intake of
specific food items and animal products. Neumann, Gewa, & Bwibo (2012) mentions negative
cultural beliefs, in addition to poverty, lack of availability and accessibility of animal food, as
one of the main causes of micronutrient deficiency among infants in developing countries.
Cultural and beliefs affect health attribution and ultimately determine choice of remedy. Someauthors have argued that poverty must not always be blamed for the non-adoption of modern
preventive and curative health care as the cost is sometimes within the means of the poor. But
7

rather non-adoption must be blamed on social and cultural beliefs concerning child healthoutcomes (Feyisetan & Adeokun, 1992).
In Lao PDR, different ethnic groups have certain beliefs that may influence child and maternal
health. Holmes, Hoy, Lockley, Thammavongxay, KBounnaphol, Xeuatvongsa, Toole (2007)
report that most Khmu mothers discard colostrum for 1-6 days for the fear of causing diarrhea.
The baby is instead fed by dipping cotton bud into honey or glucose for the period until white
appears and some infants are also fed chewed rice or rice soup from day one. In other instances,some foods are forbidden, it is believed among the Lue that egg delay teething or cause tooth
problems. Also, Sa et al. (2013) in a study on the cultural influence on the food security-nutrition
nexus among children of the Khmu ethnic group in Lao PDR, observes wide variation in thefeeding practices of both mothers and children. Notable among the restrictions is the feeding
taboos during pregnancy and post partum and the early introduction of pre-lacteal foods. These
cultural beliefs may influence childcare and feeding habits of mothers and infants.
Of the factors that affect child health outcomes, dietary diversity and nutrition have received
attention in recent times. Dietary diversity is particularly important for infants and young
children who need nutrient and energy-dense food for healthy growth as well as physical andmental development. It is recommended that breast milk should be supplemented with nutritious
diet after 6 month of exclusive breastfeeding. To this end most infant feeding guidelinesemphasis dietary diversity as a way of improving nutritional status of children. To test thishypothesis Arimond and Ruel (2004) used data from the Demographic Health survey for 11
countries. After controlling for household socioeconomic status, the study found a positive
association between diversified diet and child nutritional status. An earlier study by (Onyango,
Koski, & Tucker (1998) also found that it is important to ensure dietary diversity among children
aged 1 to 3. The findings of these works therefore suggest that public health interventions forchildren should also emphasis dietary diversity in addition to exclusive breastfeeding.
This paper examines the relationship between child malnutrition in Lao PDR and food security,
mother’s education and cultural differences. Specific hypotheses tested in this paper are as
follows: (i) consumption of a diversified basket of food (made up of a count of the nine fooditems) yields consistent results across all three anthropometric indicators compared to theconsumption of individual food items; (ii) cultural practices that impose restriction on the foodconsumption/habits and childcare contribute to malnutrition and (iii) mothers with higher
education have children with better nutritional status. In addition to these hypotheses, thisexercise explores the relationship between ownership of agricultural land/size and malnutritionin Lao PDR. The latter is exploratory because land is one of the variables that have been used in
capturing wealth of the household in the LSIS data and therefore its effect is swept away whenboth variables are included in a model. Also, the measurement of the land size using hectares
lumps up most of the households (about two-thirds) as having less than two hectares. This masks
the differences in land size across households and therefore restricts our understanding of theeffect of the potential effect of land on malnutrition.

8

Motivation and guidance for this exercise is drawn from the sluggish reduction of malnutrition in
Lao PDR and also from the exiting studies that have established the likelihood of these factors in
influencing child malnutrition in other countries: food security (Arimond & Ruel, 2004; Steyn,
Nel, Nantel, Kennedy, & Labadarios, 2006); mother education (Cochrane, Leslie, & O’Hara,
1982; Thomas, Strauss, & Henriques, 1991).
The rest of the paper is organized as follows. The next section considers the methodology
adopted in this exercise. This is followed by the analyses and discussion of the results and lastly
conclusions are drawn with policy recommendations and implications.
Methodology
Data
Data for the study was extracted from the Lao Social Indicator Survey (LSIS). The Ministry of
Health and the Lao Statistics Bureau carried out this survey in 2011/2012. The LSIS (2012) is ahousehold survey that applied the technical framework of both the Multiple Cluster Indicator
Survey (MICS) and Demographic Health Survey (DHS). It thus provides up to date informationon the health, social and economic circumstances of children, women and men : specificquestions are related to health, nutrition, education, water and sanitation, marriage and sexual
activity, fertility and mortality, contraception, HIV/AIDS, child protection, and use of mass
media and information technology (LSIS, 2012).
In all 19,960 households were selected for inclusion in the survey out of which 18,843 were
eventually interviewed. Within these households 22,476, 9,951 and 11,067, females, males andchildren respectively were interviewed. Out of the 11,067 children interviewed 10,162, 10,293
and 10,099 respectively were used for the weight-for-height, height-for-age and weight-for-age
analyses. Even though these are significantly less than the total sample in the survey, oursubsample is representative because the results from the subsample are consistent with the resultsin the report of the survey (see Figure 2, pp. 3)
Measurement of variables
This section describes measurement of the main variables used in the study i.e. food diversity,
nutritional status (stunting, wasting and underweight), ethnicity and mothers education.
Food Consumption diversity: In the last decade, a variety of measures of food consumption
diversity have emerged from different sources namely, academic researchers, World Food
Programme (WFP), World Health Organization (WHO), United States Agency for International
Development (USAID), and Food and Agriculture Organization (FAO). Primarily, the measures
have focused on the consumption of different food groups and the unit of observation has been
either the household or the individual. While all the measures underscore the importance ofcounting the number of different food groups, applying weights and including frequency of
consumption in the measurement are some of the sources of variability across the measures.

9For children, specifically those aged between 6 – 23 months, WHO (2010) recommends the useof a minimum dietary diversity (MDD) and minimum meal frequency (MMF) as indicators forfood consumption diversity. The measure is based on a count of seven food groups namely,
grains, roots and tubers; legumes and nuts; dairy products (milk, yogurt, cheese); flesh foods(meat, fish, poultry and liver/organ meats); eggs; vitamin-A rich fruits and vegetables; otherfruits and vegetables and based on counts a child consuming any food item in at least four of thefood groups is classified as consuming a diversified diet. To complement the use of counts, theMMF number of times breastfed and non-breastfed children receive solid, semi-solid, or soft
foods or milk feeds has been recommended. The use of frequency is consistent with the WFP’s
computation of food consumption.
In view of the absence of a wide variety of food items in the LSIS survey for the construction offood groups, adopting fully the WHO approaches for computing food consumption diversity forchildren was a challenge for the current study. To partially surmount this challenge, but stillusing either the count or the frequency approach, consumption of breast milk, infant formula,yoghurt, semi-solid food, juice/juice drink, porridge, liquids from bottle with a nipple, vitamin or
mineral supplement and other liquids in the last day preceding the survey was employed tocapture food diversity score for children less than five years. Given the number of food items thescore ranged between 0 and 9 implying that children with higher scores consumed a morediversified diet. Since the contribution of some of the food items such as liquids from bottle witha nipple and other liquids were not specific, sensitive variants (use a maximum of seven, eight ornine) were explored to assess variability in the estimated signs and coefficients. The results
remained the same for instances where the basket of food items excluded liquids from bottle witha nipple and other liquids.
Nutrition status (Anthropometric indicates): Nutrition status of the child is measured using three
anthropometric indicators, each capturing a different aspect of malnutrition. Stunting (height-forage) is used as an indicator of chronic undernutrition attributable to prolonged food deprivation,
wasting (weight-for-height) caused by more recent food deprivation and underweight (weightfor-age) is an indicator for both acute and chronic undernutrition. These indicators are captured
as z-scores with values in range ofand thresholds for classifying nutritional status (WHO,2006). Subsequently a child is classified as stunted, wasted or underweight if he/she had z-scoresless thanstandard deviations for height-for-age, weight-for-height and weight-for-age
respectively. Binary outcome of these anthropometric indicators are used for descriptive
analyses while the raw z-scores (continuous) partly warrants the engagement with least squares
regression analysis.
Ethnicity: The ethnicity effect on child nutrition outcome was captured in this study with a set ofdummy variables for the ethnic group of the household head. All household heads and for thatmatter children belong to Lao Tai, Khmu, or Hmong. Household heads belonging to other ethnic
groups were put in a fourth category called other.

10Mother’s education: The mother’s educational attainment was used to measure the effect ofmother’s knowledge on the appropriate care required to promote acquisition of better nutritional
status for children. Highest level of schooling of the mother was used as a measure foreducational attainment. Those with no formal education or preschool were put into one category
and labeled as “none” and were assigned the value 0. The other categories are primary, lower
secondary, upper secondary, Post-secondary and Higher (Tertiary education). The expectation isthat mother’s with higher education have better child knowledge which translates into improved
child’s nutritional status.
Model specification and estimation techniqueThis section presents the empirical model that was estimated as well as the estimation techniqueused to estimate the model. As indicated above we run three models for each of the nutritionindicators. Based on the reviewed literature and the purpose of this study we estimate thedeterminants of child nutrition as:

where:=food diversity score;=mother’s education;=mother’s age= ethnicity of head of the household;=whether the household is inan urban area;=source of potable water;= type of toilet facility;=number of kids in the household;=whether child had fever in the last
two weeks;=whether child had diarrhea in the last two weeks; sex=thesex of the;= wealth index of the household and=province fixed
effect.

In the model,. The link between food diversity, mother’s education andethnicity and child health outcomes is investigated using Ordinary Least Squares (OLS). The useof OLS is appropriate in this instance because the dependent variables are continuous. The raw zscores are used because it allows us to determine changes across observations instead ofcategories.
Estimating the above equation is based on the assumption that all variables in the model arestrictly exogenous, however, food diversity is likely to be endogenous as a result of a bi-causal
relationship with the health status of children. Since choice of feeding practices adopted by childcare givers is informed by the health status of children, bi-causality is plausible. In this case, thedirection of causality will run from the health status of the child to the choice of feedingpractices. In view of this, Two Stage Least Square (TSLS) estimation technique was employed tocontrol for the endogeneity. The number of poultry, goats and sheep the household owns as well
as household ownership of bank account were used as instrument for food diversity. The
underlying intuition of the choice of these instruments is based on the view that their effect is11directly related to food security and indeed the relationship between the instruments and thehealth status of children is only through food consumption.
To employ TSLS, the following two models were estimated:and̂

where ̂ is the linear prediction food diversity score from expression (2). In Table 1 below
we explain how the explanatory variables were defined and measured.

12

Table1: Definition and measurement of variables

Variable
Male

Definition
Gender of child

Measurement
0=Female; 1=MaleFever

0=No; 1=Yes

Mother's Age

Was child ill with fever inlast 2 weeks
Did child have diarrhea inthe last 2 weeks
Number of kids in thehousehold
Residence status of thehouseholdAge of the child's motherChild’s age

Age of child in categories

Measured as: 0=0-5; 1=6-11; 2=12-23;
3=24-35; 4=36-47;5= 48-59

Mother’s education

Mothers highest educational
attainment

0=None;1= Primary; 2=Lower secondary;
3=Upper secondary; 4=Post-secondary non
tertiary; 5=Higher

Wealth quintile

An index of the wealthstatus of the household

0=Poorest; 1=Second; 2= Middle;
3=Fourth; 4=Richest

Ethnicity

Ethnic group to which thehousehold head belongs

0= Lao; 1=Khmu; 2=Hmong; 3=Other

Water source

Household’s source water

0= Pipe; 1=Protected; 2=Unprotected

Toilet typeType of toilet facility ofhousehold0=Flush; 1= Pit latrine;2==Bush, Bucket
and Other

Province

province of household

Set of dummy variables to capture theadministrative regions in Lao PDR

Food diversity

Food diversity score

Measured as simple count of food items

Diarrhea
Kids
Urban

0=No; 1=Yes
Count of children less than 5 years
1=Urban; 0=Rural
Measured in years. Ranges from 15-45
years

Results and discussion
As a recall the objective of this exercise is to find the effect of food security, mother’s educationand ethnicity on child’s health. This section presents the test for bivariate associations(descriptive) and conditional effect of each of the three main variables of interest (Least Squares)
on nutritional status of children. For both the descriptive and inferential analyses three outcomes13

of nutritional status namely height-for-age (stunting), weight-for-height (wasting) and weightfor-age (underweight) are used.
Table 2 presents prevalence rates of correlates of malnutrition as per the objectives of thisexercise. On the association between food security and nutritional status two of the three
anthropometric indicators (stunting and underweight) were statistical significant. For children
who did not consume any of the food items and those who consumed at most any one of thefood items nearly half for each of these two groups of children were stunted. For this samegroups of children, about a-third of were underweight. In both instances, that is proportions ofstunted and underweight children a decreasing relationship with food diversity was observed.
That is more diversified consumption of a basket of food items was associated with lower
prevalence rates of stunting and wasting..
The Table shows significant association between child stunting and underweight on one handand mother’s educational status on the other hand. The analysis shows that more than half ofchildren from mothers with no formal education are stunted and 37 percent of them have weightconsidered too light for their age. As expected, the Table indicates that advancement in maternal
education reduces the incidence of malnutrition. This finding supports an earlier argument by(Kamiya, 2011). In this study Kamiya (2011) argue that education level of parents affects childhealth outcomes in Lao PDR.
In all the three cases nutritional status tend to significantly vary across the ethnic divide. The
ethnic group with the highest proportion of stunted children is Hmong (61 %) followed by Khmu
(53.8 %). But we observe a different pattern in the case of underweight, in this instance morethan a quarter of Khmu children are considered as underweight while one-fifth of Hmongchildren underweight. In a reverse finding, Hmong has the lowest proportion of child wasting.
These figures are consistent with the finding in the Lao Social Indicators Survey [see Table NU.1of the LSIS Report (2012)].

14

Table 1: Bivariate analysis of socioeconomic variables and anthropometric indicators
Socio-economic and demographic
variables
Age
0-5
6-11
12-23
24-35
36-47
48-59
Chi squareSex
femalemale
Total
Chi square
DiarrheaNo
Yes
Chi square
Food diversity
None
At most One
At most two
At most three
At most fourMore than four
Chi square
Residence
Rural
Urban
Chi square
Ownership of Agriculture LandNo
Yes
Chi square
Fever
No
Yes
Chi square
Wealth index quintile
PoorestSecondMiddleFourthRichestChi square
Mother's education
None
Primary
Lower secondary
Upper secondary
Post secondary non tertiaryHigherChi square

StuntedNo. of%Children

WastedNo.
%of Children

UnderweightNo.
%of Children

18.2 19422.7 242
44.8 87851991
51.9 1089
54
1031
701.62 (Pr = 0.000)

6.5
68
8.4
88
7.6
154
7.4
146
4.9
105
3.9
76
43.45 (Pr = 0.000)

11.7 12618
19525.6 51230.1 591
30.8 650
31.4 602
240.84 (Pr = 0.000)

42.5 2,109
45.7 2,315
44.1 4,424
10.85 (Pr = 0.001)

5.8
290
6.7
347
6.3
637
5.04 (Pr = 0.025)

26.1
26.6
26.4
0.43

43.4 3,910
50.2 514
16.91 ( Pr = 0.000)

6.3
570
6.1
67
0.02 (Pr = 0.876)

25.7 2,339
32.5 337
13.11 (Pr = 0.000)

59.5 2048
1216
49.1 168844.2 880
37357
24.5 263
212.93 (Pr = 0.000)

6.9
2
7.3
205
5.6
207
5.8
110
7
62
5.8
51
9.97 ( Pr = 0.076)

48.3
29
28.6
26.4
21.3
16.7
96.85

48.6 3,866
26.9 558
287.99 (Pr = 0.000)

6.5
535
5.4
102
2.27 (Pr = 0.131)

29.2 2,349
15.6 328
169.83 (Pr = 0.000)

40.5 1,066
45.4 3,358
16.23 (Pr = 0.000)

6
147
6.4
490
0.38 (Pr = 0.537)

23.9 634
27.3 2042
14.98 ( Pr = 0.000)

44.3 3,814
42.6 610
0.11 (Pr = 0.740)

6.1
533
7.2
104
2.42 (Pr = 0.120)

26.1 2,270
28.1 407
3.66 (Pr = 0.056)

60.7 1,803
49.7 1,070
41.5 761
31.7 507
19.2 283
801.51 (Pr = 0.000)

7.2
241
6.5
150
5.9
110
5.3
78
5
58
10.33 (Pr = 0.035)

37
29
24
20
12
06.50

58.2 1,840
43.4 1,801
33.8 508
22.3 144
29
98
14.1 33
519.35 (Pr = 0.000)

6.3
217
6.3
267
6.9
97
4.8
26
6.6
21
4.5
9
4.69 (Pr = 0.455)

35.1 1,131
26
1,090
20.2 304
12.6 82
17.9 614.1
10285.87 (Pr = 0.000)

15

1,309.2
1,367.4
2,676.6(Pr = 0.514)

16
746
998
529
207
180
(Pr = 0.000)

1,109
637
446
315
170
(Pr = 0.000)

Ethnicity of household headLao
31.8 1,475
Khmu53.8 666
Hmong
61
757
Other
52.2 1,527
Chi square552.82 (Pr = 0.000)
Source of Drinking Water
pipe
37.4 1,642
protected
48.3 1,219
unprotected50
1,564
Chi square
118.90 (Pr = 0.000)
Type of Toilet Facility
Flush
34.1 1,656
Pit latrine
52.5 167Bush, Bucket and Other
53.5 2,601
Chi square358.51 (Pr = 0.000)
Province
Vientiane Capital
19.1 177
Phongsaly
61.5 200
Luangnamtha
54.1 139
Oudomxay
54.7 357
Bokeo
45.7 139
Luangprabang
45
301
Huaphanh
61.1 345
Xayabury
38.5 176
Xiengkhuang
52.7 264
Vientiane41.3 287
Borikhamxay
39.6 148
Khammuane41.5 233
Savannakhet
40.8 644
Saravane
54.6 466
Sekong63.3 153
Champasack
36.2 316
Attapeu
39.5 79
Chi square
461.89 Pr = 0.000
Total
44.1
Source: Computed from LSIS Data (2011)

6.2
240
5.4
75
2.5
34
8
288
54.72 (Pr = 0.000)

20.8 972
28.8 362
21
263
36.3 1080
322.91 (Pr = 0.000)

5.7
245
6.5
186
6.9
206
6.00 ( Pr = 0.050)

20
886
30.5 779
31.9 1012
159.16 (Pr = 0.000)

5.4
254
2.2
9
7.4
374
27.20 (Pr = 0.000)

19932
29.2 9433.5 1650
278.37 ( Pr = 0.000)

7.4
36
5
26
21.8 104
5
37
4.2
23
3
18
2
13
5.4
23
1.9
11
4.6
26
6.4
28
6.9
42
4.9
40
7.3
57
7.5
61
6.3
39
10.2 53272.61 Pr = 0.000
6.3

15.9 148
34.7 11739.9 10628.4 188
2371
19.9 135
23.3 132
22.5 10519.8 10018.6 13019.4 73
29.8 169
27.4 439
41.4 357
46.5 115
26
229
32
64
366.06 Pr = 0.000
26.4

Testing the association between child’s age and malnutrition prevalence, 18.20 percent ofchildren within zero to five months are stunted, 11.70 percent of them are underweight and 6.50
percent are wasted. Generally, the proportions of children who are stunted and underweightincrease with an increase in child’s age. This pattern could be because older children receive lessattention in terms of feeding practices and childcare. However, the same cannot be said withchild wasting, in this instance the age category with the highest proportion of children are
between six and eleven months while those aged 48-59 months had the lowest proportion ofunderweights. Thus while child stunting and underweight may be a problem with older children,
wasting is rather associated with younger children. In all the three instances the chi square tests
show that anthropometric indicators vary significantly across age categories.

16

Agricultural land ownership and malnutrition

Since agriculture is an important source of livelihood of most Laotians, the study investigates therelationship between ownership of arable land malnutrition in Figure 5. The figure shows thatmajority of children who are stunted (60%); wasted (55%) and the underweight (58%) belong tohouseholds who have less than two hectares of arable land. Increasing the size of agriculturalland also reduces the incidence of malnutrition. For instance about 20 percent, 23 percent and 22
percent of the stunted, wasted and underweight children respectively are found in households
that have 2 hectares of arable and the proportions fall to nine percent, 10 percent and nine
percent respectively for households that possess more than three hectares of arable land. Accessto agricultural land is associated with access to adequate food supply which in turn reduces childmalnutrition. Land ownership has been shown to reduce severe malnutrition in Bangladesh(Choudhury, Hanifi, Rasheed, & Bhuiya, 2011). In a related study elsewhere in Tajikistan,
Baschieri and Falkingham (2007) indicated that living in households without access to land
increases the risk of underweight by almost 50 percent.

100%
Prevalence of Malnutrition

90%
80%
70%

9.03

8.96

9.13

11.09

12.98

11.06

23.03

22.03

19.84

60%
50%

More Than Three
Hectares40%

Three Hectares

30%

60.05

57.75

55.03

Two Hectares

20%
10%

Less than 2 Hectares

0%
StuntedWastedUnderweight

Types of Malnutrition

Figure 5: Land ownership and child malnutrition

17

Figure 6 presents the distribution of food diversity score in Lao PDR. About 60 percent ofchildren consumed at most two of the food items, 29 percent consumed either three or four of thefood items 11 percent were fed with more than four food items. That is, most children less than
five years consume less diversified food items in Lao PDR. Because there is the tendency forchildren who are undergoing exclusive breastfeeding to distort the overall distribution theanalysis is done for various age categories in Figure 6. More than half (59 %) of children lessthan six months had at most 1of the nine food items and only 2 percent consumed all the nine
food items. For the more than half of children consuming at most one of the food items it isexpected, as children in this age bracket should be exclusively breastfed. However, it is worth
exploring the implications of children less than six months who are not undergoing exclusive
breastfeeding. Broadly comparing children less than six months by those between six and 59
months, there is a change from the consumption of monotonous to diversified food items. An
association between food consumption and age of the child is supported with a p-value of 0.00
for the statistical chi-square value of 695.29.

100
90

2.33
4.62

13.61

10.68
11.16

80
70

23.52
21.12

60

11.18

10.79

9.96

9.64

20.59

19.5

50
40
30

33.04

37.13

20.97

20.71

34.19

58.49

20

10
0

0.36
Up to 6 months
None
At most one

0.43
0.1
7-24 Months
25-59 Months
At most two
At most three
At most four

Figure 6: Food diversity score by age groups

18

25.55
0.32
Total
More than four

4.50

4.10

4.00

Food Diversity Score

3.50
2.83 2.90 2.94

3.00
2.50
2.00

2.04 2.09
1.60 1.68

2.20 2.20 2.23 2.23

2.41 2.48

3.09

2.59

1.79

1.50
1.00
0.50
0.00

Province

Figure 7: Distribution of food diversity score by provinces in Lao PDR
For the purpose of validation the computed food diversity is analysed from provincial
perspective and compared with the recent Risk and Vulnerability Survey (RVS) conducted in
2013. In spite of the fact that the timing of the two surveys vary and also given that the RVS
sample is inclined towards rural households finding broad patterns remains essential forproceeding with this exercise. The RVS captures food diversity based on a similar approachcount of number of food items consumed by children less than five years. Further to this, adummy for children consuming less than four food items is generated and classified asconsuming sub-optimal dietary diversity. Using the agro-ecological zoning the RVS (2013)
report indicates that 76.8 percent of all children less than five years in the Central-SouthernHighlands (i.e. Khamouane, Savannakhet, Saravane, Sekong and Attapeu) were fed with suboptimal dietary diversity. In our case Sekong and Attapeu Savannakhet, Saravane rank among
the provinces with low food diversity scores. RVS (2013) also reports that Vientiane Plain (i.e.
Vientiane, Borikhamxay, and Khammuane) is the agro ecological zone with the least number ofchildren being fed with sub-optimal dietary diversity. Similarly from Figure 7, children in
Vientiane, Bolikhamxay, and Khammuane on the average have a food diversity score of morethan one. Thus, provincial patterns of the summary of food diversity score proposed in thisexercise are broadly consistent with the agro-ecological patterns of dietary diversity in RVS(2013) report.
19

Results of multivariate regression
In Table 3, we estimate the effect of mother’s education, food diversity and ethnicity on childhealth outcomes. As indicated in the methodology dietary diversity is captured by a count of
number of food items consumed. The computation of this score is such that an increase in thescore means more diversification hence better nutrition intake. This index was constructed fromnine food items: infant formula, breastmilk, yoghurt, semi-solid food, juice/juice drink, porridge,
liquids from bottle with a nipple, vitamin or mineral supplement and other liquids. The
motivation for this variable is derived from the understanding that diversified diet enhances thelikelihood of meeting nutrient requirements. The association between food diversity score andthe three measures are significant all the indicating that dietary diversity is important in
explaining child nutrition. This association is an indication that dietary diversity is an indicatorof nutrient adequacy among child in Lao PDR. Our findings is supported by a similar study by(Arimond & Ruel, 2004). Arimond and Ruel (2004) found in seven (including Cambodia and
Nepal) out of the 11 countries studied that dietary diversity is significantly associated with better
nutritional status.
Ethnicity was significant in explaining child health outcomes. In the case of child stunting,children from Khmu and Hmong had lower HAZ scores than those from Lao-Tai ethnic group.
For HAZ, the analysis shows that Hmong children had the worse z-scores than children from
Lao-Tai and other ethnic groups. Conversely children from Khmu and Hmong had better
weight-for-height z-scores than Lao-Tai children. In the case of underweight (weight-for-age)children less than five years among the Hmong had better nutrition than their counterparts
belonging to the Lao-Tai ethnic. The patterns suggest that malnutrition for ethnic groups varyacross the three anthropometric indicators. Feeding restriction practices observed by Sa et al(2013) among the Khmu that is pregnancy and post partum food taboos and early introduction ofpre-lacteal feeding can be attributed to the observation that Khmu children are too short for theirheight.
In line with the objectives of the study, we proceed to examine the relationship between maternal
education and child malnutrition. To this end, mother’s level of education was used to capture
the education effect in the models. The expectation is that as mother's education is associatedwith an understanding of the importance of good feeding and childcare practices enhance thenutritional status of their children. Again education also promotes access to health informationfrom the mass media and also promotes community participation. Using mothers who have noformal education as the base category, we find that children from mothers with primary, lower
and upper secondary and Higher education have significantly higher HAZ scores as compared tochildren from mothers with no formal education in the OLS models. In the WAZ model thedifference is significant between children from mothers with primary, upper secondary andHigher and higher education on one hand and no formal education on the other hand. This goesto confirm the assertion that education is an important component of the health production
function (Frost et al., 2005; Thomas et al., 1991; Variyam, Blaylock, Lin, Ralston, &Smallwood, 1999).
20

The models also contain a number of control variables that may affect child health outcome
according to the literature reviewed. It can be observed from the Table that child’s age
significantly correlate with child health outcomes. Compared to the base age of 0-5 months, themodels show that all the three indicators consistently decrease as a child’s age increases. Thisfinding is corroborated by an earlier work by (Babatunde, Olagunju, Fakayode, & Sola-Ojo,2011). In this study Babatunde et al (2011). found that an increase in a child’s age increases theprobability of being stunted in Kwara State in Nigeria.
Since the current health status of a child can affect his nutrition status, we control for the current
health status with two common illnesses (diarrhea and fever) among children. These illnesses
tend to reduce anthropometric scores in all the models. The effect of diarrhea is significant in all
the models; however, fever is significant only in affecting wasting and underweight. The effectof sex is captured with the male dummy variable. The sign and coefficient of the male variable
shows that male children have higher chance of being stunted compared to female children.
Specifically, males have 0.10 less height-for-age scores than females. This finding is not
surprising since boys have been shown to have high probability of being malnourished than girls(Masiye, Chama, Chitah, & Jonsson, 2010; Wagstaff, Van Doorslaer, & Watanabe, 2003).
The HAZ and WHZ models show that children from households with large number of children
have poorer nutritional status compared their counterparts from households with smaller numberof children. In these models both height-for-age and weight-for-age decreases by approximately
0.03 for any additional child. The likely reason for this association could be that as the number ofchildren increase parents and caretakers pay less attention to each individual child in terms of
feeding and cleaning which will negatively affect their nutritional intake. Also large number ofchildren also means that kids will have to share limited amount of food resources leading to
insufficient food intake.
To further understand the correlates of child nutritional status, household’s wealth was includedin the models in Table 3. In the case of the HAZ and WAZ models, children in second, middle,
fourth and highest categories have better nutritional status than children in poorest households.
However, in the underweight model only children in highest households tend to have better
health outcome than those in poorest households.
Access to safe water was included as an environmental variable. Children in households which
use protected and unprotected water sources have lower WAZ and HAZ score than theircounterparts from households that have access to pipe born water. Access to clean waterpromotes good hygiene which improves child’s health (Baschieri & Falkingham, 2007)21

Table 3: Regression analysis on child’s consumption of diversified food items andnutritional Status in Lao PDR

Male
Fever
Diarrhea
Food diversity
Kids
Urban
Mother’ ageChild’s age (base=0-5)
6-11
12-13
24-35
36-47
48-59
Mother’s edu. (base=None)
Primary
Lower Secondary
Upper Secondary
Post sec. non tertiaryHigher
Wealth (base=Poorest)
SecondMiddleFourthRichestWater (base=Pipe)
Protected

Weight –for-Height
(OLS)
(IV)
0.02
0.03
(1.00)(0.93)-0.18*** -0.21***(-5.06)(-4.22)-0.09*-0.10*(-2.20)(-2.06)+0.02
0.18(1.75)(0.97)-0.01-0.00(-1.33)(-0.25)0.02
0.02(0.50)(0.37)
0.00-0.00(0.90)(-0.05)Weight-for-Age
(OLS)
(IV)
-0.01
-0.00
(-0.43)(-0.16)-0.14***-0.21***(-4.08)(-3.76)-0.11**-0.13*(-2.72)(-2.48)
**
0.04
0.34+
(3.28)(1.70)-0.03***-0.01(-3.42)(-1.45)0.04-0.00(0.94)(-0.04)0.01***
0.01**(4.59)(3.16)Height-for-Age
(OLS)
IV)
**
-0.10
-0.09*(-3.16)(-2.34)-0.02-0.13+(-0.56)(-1.73)-0.10+-0.11+(-1.81)(-1.76)
**
0.04
0.50+
(2.82)(1.94)-0.03*-0.01(-2.38)(-0.61)0.08-0.00(1.57)(-0.06)0.01***
0.01***(4.95)(3.63)-0.52***(-8.30)-0.58***(-10.47)-0.49***(-8.70)-0.46***(-8.52)-0.52***(-9.63)-0.68***(-4.23)-0.78***(-3.99)-0.71***(-3.79)-0.69***(-3.74)-0.71***(-4.08)-0.44***(-6.97)-0.73***(-12.73)-0.85***(-14.99)-0.91***(-16.69)-1.01***(-18.45)-0.68***(-3.80)-1.02***(-4.75)-1.15***(-5.56)-1.20***(-5.81)-1.28***(-6.53)-0.32***(-4.17)-0.99***(-13.75)-1.22***(-16.99)-1.28***(-18.82)-1.27***(-18.67)-0.69**(-3.10)-1.42***(-5.13)-1.62***(-6.18)-1.67***(-6.36)-1.67***(-6.66)0.01(0.20)-0.04(-0.81)0.03(0.39)-0.01(-0.09)0.02(0.20)-0.03(-0.55)-0.11(-1.26)-0.03(-0.27)-0.09(-0.50)-0.05(-0.31)0.07*(2.05)
0.09+(1.81)
0.15*(2.30)
0.01(0.12)
0.24*(2.41)

0.01(0.25)-0.05(-0.46)0.06(0.47)-0.24(-1.22)-0.03(-0.15)0.13**(2.99)
0.17**(2.90)
0.21**(2.68)
0.02(0.22)
0.34**(2.83)

0.03(0.49)-0.04(-0.29)0.06(0.36)-0.44+(-1.74)-0.24(-1.03)0.04(1.17)
0.04(0.93)
0.01(0.27)
0.14*(2.21)

0.03(0.58)-0.00(-0.05)-0.05(-0.42)-0.04(-0.19)0.18***(4.61)
0.24***(5.69)
0.28***(5.37)
0.49***(7.23)

0.11*(2.23)
0.14*(2.07)
0.12(0.93)
0.14(0.69)

0.25***(5.26)
0.35***(6.48)
0.45***(6.59)
0.62***(7.32)

0.13*(2.21)
0.19*(2.14)
0.15(0.95)
0.13(0.50)

0.04

0.04

-0.05

-0.06

-0.10*-0.11*

22

Unprotected
Toilet (base=Flush)
Pit latrine
Bush, bucket, other
Ethnicity (base=Lao-Tai)
Khmu
Hmong
Other

Phongsaly
Luangnamtha
Oudomxay
Bokeo
Luangprabang
Huaphanh
Xayabury
Xiengkhuang
Vientiane
Borikhamxay
Khammuane
Savannakhet
Saravane
SekongChampasack
Attapeu
Constant
P
N

Hansen J statistic(1.07)-0.04(-1.10)(1.03)-0.01(-0.14)(-1.33)-0.09**(-2.61)(-1.28)-0.06(-1.30)(-2.10)-0.08+(-1.80)(-1.98)-0.04(-0.68)0.01(0.13)-0.07+(-1.90)0.06(0.96)-0.06(-1.31)0.12+(1.93)-0.04(-1.20)0.16*(2.27)-0.02(-0.35)0.10(1.26)-0.01(-0.12)0.14(1.52)
0.03(0.48)

0.16**(3.09)
0.53***(10.54)0.06(1.64)

0.17*(2.31)
0.56***(9.31)
0.06(1.30)-0.03(-0.52)0.18***(3.43)-0.10*(-2.49)0.02(0.23)
0.20**(3.15)-0.07(-1.38)-0.19**(-2.98)-0.31***(-4.89)-0.22***(-4.69)-0.11(-1.02)-0.28***(-3.43)-0.15*(-2.41)0.40***(4.77)-0.24*(-2.36)
0.21**(2.64)
0.13+(1.74)
0.28***(3.67)
0.35***(4.78)
0.24**(2.92)
0.34***(4.51)
0.22**(2.87)
0.11(1.43)-0.10(-1.42)0.12+(1.71)-0.09(-1.14)0.03(0.41)-0.07(-0.90)-0.25***(-3.32)-0.20+(-1.80)10162

0.59***(4.31)-0.07(-0.51)0.50*(2.47)
0.31*(2.01)
0.37***(3.91)
0.62**(2.59)
0.43**(3.00)
0.55**(3.11)
0.36**(3.02)
0.25*(2.25)
0.08(0.55)
0.31+(1.91)
0.05(0.34)
0.25(1.21)
0.05(0.46)-0.04(-0.19)-0.48(-1.36)8246-0.10(-1.16)-0.43***(-4.94)-0.02(-0.31)0.08(1.00)
0.12(1.46)-0.04(-0.48)-0.08(-1.02)0.08(1.07)-0.05(-0.74)0.02(0.31)-0.16*(-2.22)-0.08(-1.14)-0.30***(-3.75)-0.40***(-5.25)-0.15*(-2.05)-0.14+(-1.83)-0.93***(-8.58)10293

0.18(1.15)-0.19(-1.31)0.42+(1.87)
0.38*(2.21)
0.22*(2.02)
0.44+(1.66)
0.20(1.28)
0.45*(2.24)
0.16(1.18)
0.23+(1.82)
0.11(0.67)
0.25(1.39)-0.04(-0.24)-0.04(-0.17)0.06(0.47)
0.23(1.09)-1.56***(-3.96)8350-0.59***(-5.64)-0.28*(-2.37)-0.31**(-3.18)-0.09(-1.01)-0.24**(-2.65)-0.49***(-5.43)-0.28**(-2.91)-0.29**(-3.10)-0.43***(-4.71)-0.21*(-2.17)-0.12(-1.34)-0.22*(-2.43)-0.42***(-4.51)-0.69***(-7.20)-0.22**(-2.62)0.02(0.20)-1.06***(-7.57)
10099

-0.30
(-1.57)-0.02(-0.11)0.24(0.84)
0.27(1.26)-0.18(-1.36)0.15(0.44)
0.03(0.17)
0.18(0.72)-0.17(-0.97)0.02(0.11)
0.21(1.03)
0.21(0.95)-0.07(-0.35)-0.18(-0.62)0.04(0.26)
0.50+(1.90)-1.97***(-3.88)
8190

5.36
23

3.76

5.29

P-value of Hansen J statistic
Underidentification test
P-value of underidentification test
Weak identification statistic

0.15
19.66
0.00
9.72

0.15
19.51
0.00
12.92

0.15
19.24
0.00
9.54

t statistics in parentheses; +p<0.1; * p<0.05; ** p<0.01; *** p<0.001

Taking cognizance of the likely endogeneity that may arise from bi-causality between theanthropometric indicators and food diversity, we estimate the three models using the number ofpoultry, goats and sheep the household owns as well as the household ownership of bank accountas instruments. The TSLS estimation shows that food diversity causes improvement in both
WAZ and HAZ scores. At 10 percent level of significance, Table 3 shows that food diversity
causes both weight-for-age and height-for-age to increase by 0.34 and 0.50 units respectively.
The relationship between food consumption and child nutrition can be a bit misleading given thefact that feeding pattern varies across different age categories. For instance, nutritional status ofchildren less than 6 months and undergoing exclusive breastfeeding will have no business withthe consumption of semi-solid food. This partly contributes to the estimation of a variant of themodels in Table 4. In Table 4 we estimate the model using selected individual food items
namely, infant formula, milk and yoghurt which are common food item that infants are fed on in
Lao PDR. Infant formula and milk are significant in explaining wasting, stunting and childunderweight. In each model, milk and infant formula tend to improve child health outcome.
However, yoghurt has a positive association with only child’s weight-for-age z-scores.
To further explore the relationship between food consumption and nutrition status of children,
estimations in Table 3 are replicated using a sub-sample of children aged 24 to 59 months andless than 6 months (see Appendix B). The rationale is to explore the effect of the consumption ofindividual foods items such as semi-solid food which is consumed at a later stage in the growth
cycle of a child. Appendix B presents results of the sub-sample for the effect of the consumptionof individual food items on nutritional status. Consumption of semi-solid food tends to besignificant for but with a counterintuitive result. Child ever breastfed is significant in both WAZ
and HAZ models for children less than 6 months

24

Table 4: Regression analysis on child’s consumption of individual food items andnutritional status in Lao PDR
Dependent variables
Male
Fever
Diarrhea
Infant formula
Milk
YoghurtKids
Urban
Mother’s age
Child’s age (base=0-5)
6-11
12-13
24-35
36-47
48-59
Mother’s edu. (base=None)
Primary
Lower Secondary
Upper Secondary
Post sec. non tertiaryHigher
Wealth (base=Poorest)
SecondMiddleFourth

(OLS)
WHZ
0.02
(0.98)-0.18***(-5.09)-0.09*(-2.24)0.08*(2.01)
0.09*(2.03)
0.04(0.72)-0.01(-1.25)0.02(0.41)
0.00(0.91)

(OLS)
WAZ
-0.01
(-0.52)-0.14***(-4.13)-0.12**(-2.78)0.09*(2.26)
0.17***(3.95)
0.13*(2.34)-0.03***(-3.31)0.04(0.84)
0.01***(4.64)(OLS)
HAZ-0.10**(-3.22)-0.03(-0.60)-0.10+(-1.86)0.12*(2.32)
0.17**(3.20)
0.10(1.38)-0.02*(-2.31)0.08(1.49)
0.01***(4.98)-0.51***(-8.18)-0.58***(-10.50)-0.49***(-8.78)-0.46***(-8.51)-0.52***(-9.63)-0.42***(-6.73)-0.72***(-12.82)-0.86***(-15.24)-0.91***(-16.89)-1.01***(-18.67)-0.30***(-3.97)-0.99***(-13.84)-1.22***(-17.27)-1.28***(-18.93)-1.28***(-18.83)

0.01(0.24)-0.04(-0.90)0.02(0.30)-0.02(-0.26)0.00(0.01)

0.07*(2.15)
0.08+(1.71)
0.14*(2.19)-0.02(-0.22)0.20*(2.13)

0.13**(3.07)
0.16**(2.82)
0.20**(2.59)-0.00(-0.03)0.31**(2.58)

0.04(1.20)
0.04(0.91)
0.00(0.10)

0.18***(4.66)
0.24***(5.67)
0.27***(5.08)

0.25***(5.32)
0.35***(6.47)
0.43***(6.43)

25

Richest
Water (base=Pipe)
Protected
Unprotected
Toilet (base=Flush)
Pit latrine
Bush, bucket, other
Ethnicity (base=Lao-Tai)
Khmu
Hmong
Other
Province(base =Vientiane Capital)
Phongsaly
Luangnamtha
Oudomxay
Bokeo
Luangprabang
Huaphanh
Xayabury
Xiengkhuang
Vientiane
Borikhamxay
Khammuane
Savannakhet
Saravane
SekongChampasack
Attapeu

0.13+(1.92)

0.46***(6.70)

0.59***(6.89)

0.04(1.16)-0.04(-1.07)-0.04(-1.14)-0.09*(-2.52)-0.09*(-1.97)-0.08+(-1.75)0.01(0.09)-0.07+(-1.95)0.12+(1.87)-0.05(-1.29)0.10(1.21)-0.01(-0.19)0.16**(3.14)
0.54***(10.60)0.06+(1.72)-0.02(-0.41)
0.18***(3.58)-0.09*(-2.33)-0.19**(-2.91)-0.30***(-4.76)-0.22***(-4.58)0.41***(4.88)-0.22*(-2.20)0.21**(2.71)
0.13+(1.78)
0.30***(3.93)
0.36***(4.88)
0.24**(2.93)
0.36***(4.66)
0.23**(3.00)
0.12(1.48)-0.09(-1.29)0.12+(1.76)-0.08(-0.98)0.04(0.47)-0.06(-0.79)-0.26***(-3.41)-0.09(-1.04)-0.40***(-4.66)-0.02(-0.26)0.07(0.94)
0.15+(1.84)-0.03(-0.45)-0.08(-1.01)0.10(1.25)-0.04(-0.60)0.02(0.29)-0.15*(-2.05)-0.08(-1.16)-0.28***(-3.57)-0.40***(-5.32)-0.14+(-1.89)-0.15*(-1.98)-0.58***(-5.50)-0.25*(-2.14)-0.31**(-3.14)-0.09(-1.03)-0.21*(-2.27)-0.48***(-5.42)-0.28**(-2.91)-0.27**(-2.94)-0.42***(-4.54)-0.21*(-2.14)-0.11(-1.18)-0.22*(-2.43)-0.40***(-4.29)-0.68***(-7.20)-0.21*(-2.45)0.01(0.09)

26

ConstantN-0.18+(-1.65)10162-0.89***(-8.34)10293-1.02***(-7.39)10099

t statistics in parentheses + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001

Conclusion, Policy Recommendations and Implications
This exercise has been undertaken in response to the need to hasten efforts aimed at reducing
malnutrition prevalence rates in Lao PDR. In spite of GoL’s initiative to reduce prevalence rateof malnutrition via the implementation of the NNP, recent statistics from the LSIS is alarming.
Based on WHO’s thresholds the following are inferred; 44 percent of stunted children is in thevery high range, underweight prevalence of 27 percent falls within the high bracket and six
percent of wasted children is categorized as medium. Thus none of the three anthropometricindicators falls within the low bracket. Central to the NNP in reducing malnutrition is foodsecurity hence this exercise engages the LSIS data to interrogate the relationship between foodsecurity and malnutrition. This exercise extended its orientation beyond food security to examine
other correlates of malnutrition specifically, ownership of agricultural land, cultural practices(ethnicity) and mother’s education.
Food security, one of the main variables of interest, was captured using nine food items namely;
breast milk, infant formula, yoghurt, semi-solid food, juice/juice drink, porridge, liquids frombottle with a nipple, vitamin or mineral supplement and other liquids. In the context of therelationship between food security and malnutrition, this exercise is premised on the hypothesisthat consumption of a diversified basket of food (made up of a count of the four food items)
yields consistent results across all three anthropometric indicators compared to the consumptionof individual food items. The latter is argued to be susceptible to other correlates of malnutrition
notably, age of the child. Other hypotheses tested are; cultural practices that impose restriction
on the food consumption/habits and childcare, contribute to malnutrition and mothers withhigher education have children with better nutritional status. On the relationship between
ownership of agricultural land/size and malnutrition, this study engages in an exploratory
exercise. This is because land is one of the variables that have been used in capturing wealth of
the household in the LSIS and therefore its effect is swept away when both variables are included
in a model. Also, the measurement of the land size using hectares lumps up most of thehouseholds (about two-thirds) as having less than two hectares. This masks the differences inland size across households and therefore restricts our understanding of the effect of the potential
effect of land on malnutrition.
Both bivariate descriptive and multivariate regression analyses were engaged in this exercise.
The latter is the case as child health was measured with three anthropometric indicators; Weightfor-age z-scores (WAZ), height-for-age z-scores (HAZ) and weight-for-height z-scores (WHZ).
Least Squares regression analysis instead of categorical dependent variable estimation technique
was used since the dependent variables were captured as continuous variables based on the zscores. Although cognizant of the fact that overweight constitutes malnutrition, the proportion of27

children in Lao PDR is less than two percent and therefore its effect is negligible. Sensitivity
analysis of the effect of overweight proportion on the analyses was explored and it was found not
to be significant in influencing the main variables of interest. Identification of correlates was
based on the UNICEF (1990) conceptual framework for explaining the manifestations ofmalnutrition.
On the exploratory exercise, households with land sizes less than two hectares had the highest
proportion of prevalence rates for all the three anthropometric indicators. This observation wasconsistent with the a’priori expectation as land size is a source for producing food to satisfy oneof the conditions for food security that is, its availability. GoL should in this regard, reconsider
the agenda of land titling to help households have access to agricultural land. However, since thisfinding is based on a bivariate analysis, further studies for specific locations (agro-ecologicalzones, provinces and districts) is imperative as the nature of the land is worth taking into
consideration, should a policy strategy be instigated in this direction.
Food security measured based on diversity panned out to be consistently significant across all
three anthropometric indicators. This is satisfyingly reassuring and therefore all health promotion
activities on the link between food security and malnutrition should backstop with diversity of
consumption of food items and not the production of food only. While the GoL is commended
for instituting policies to ensure food availability, household consumption of a monotonous food
item notably rice should be discouraged. The effect of individual food items namely infant milk,
milk, yoghurt and semi-solid food also influences child malnutrition. However, the effect is notconsistent for all the food items across all three anthropometric indicators. Noteworthy is the fact
that, infant milk and milk consumption yielded significant and positive results on better
nutritional status for all three anthropometric indicators and therefore these food items as atransition from exclusive breastfeeding should be promoted. While the effect of the consumptionof semi-solid food was found not to be significant for the overall sample (children less that 60
months) worth noting is the observation that for weight-for-height, this variable is significant but
counterintuitive with children in older age bracket of the sample, that is between 25 and 60
months. In the model for this sub-sample, the effect of infant milk on malnutrition is eroded.
This suggests that for this bracket, infant milk is either not consumed or even when consumed
has no effect on the child’s nutritional status.
The second variable of interest, cultural practices (measured by the different ethnic groups in
Lao PDR) was significant in explaining child health outcomes. In the case of child stunting,children from Khmu and Hmong consistently had lower WAZ scores than those from Lao-Tai
ethnic group. For HAZ, the analysis shows that Hmong children had better scores than children
from Lao Tai so do children from other tribes. Conversely, children from Khmu and Hmong,and other ethnic groups had better weight-for-height z-scores than Lao-Tai children.
The third variable of interest in this exercise, mother’s educational attainment, was observed tohave a significant effect on malnutrition. Specifically, mothers with higher education comparedto those with no education had children with better height-for-age and weight-for-age z-scores.
28

The largest coefficient in both cases of child health outcomes was observed for mothers witheducational levels higher than post-secondary. In spite of the likelihood that mothers in thiscategory would be time constrained as a result of other engagements such work, positive effects
on better nutritional status are still observed. Further exercises using case studies to understandthe channels through which mothers with high education are able to ensure that their children
have better nutritional status are advised.
Household characteristics such as wealth and access to clean water are important in explaining
child health. The study also found a negative association between number of children in thehousehold and child health outcome.
Based on the conclusions from this study, the following recommendations are made forconsideration by the three main Ministries namely, Ministry of Agriculture and Forestry (MAF),
Ministry of Health (MoH) and Ministry of Education (MoE) in Lao PDR that are at the centre of
the implementation of the Multisectoral Food and Nutrition Security (FNS) Action Plan (2014 –2020). In addition, the recommendations below will be equally useful to other governmental
institutions, development partners and civil society that are involved in nutritional interventionsin Lao PDR. Firstly, there should be a campaign for the consumption of diversified foods rather
than a single or a couple of food items. Secondly, to ensure the consumption of diversified fooditems, rearing of livestock has to be promoted. Thirdly, the consumption of diversified food andrearing of farm animals should be accompanied with adequate education on the nutritional
contents of different food items and requisite cooking practices. Thus, education on appropriate
care practices, specifically knowledge on child feeding requirements, should be improved.
Finally, malnutrition interventions should take into consideration differences across ethnic groups
for specific nutritional outcomes. One such specific consideration should be a direct campaign
against ‘unfriendly nutrition’ cultural practices such as pregnancy and post-partum food taboos
and early introduction of pre-lacteal food.

29

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31

APPENDICES
Appendix A: Summary Statistics of Variables used in Regression Analysis
Variable

Obs

Mean

Variance

Min

Max

WAZ

8350

-1.40

1.18

-5.83

4.78

WHZ

8246

-0.38

1.12

-4.88

4.91

HAZ

8190

-1.91

2.28

-5.98

5.83

Male

8190

0.50

0.25

0

1

Fever

8190

0.14

0.12

0

1

Diarrhea

8190

0.11

0.10

0

1

Number of Kids

8190

3.44

4.21

1

19

Diversity Score

8190

2.33

1.71

0

9

No. of Poultry

8190

13.94

252.33095

No. of Goats

8190

0.32

3.21

0

60

No. of Sheep

8190

0.02

0.26

0

25

Own bank Acct

8190

0.13

0.11

0

1

Wealth quint

8190

2.44

1.74

1

5

Urban

8190

0.15

0.12

0

1

Mother's age

8190

28.03

46.82

15

49

Child Age (Categorical)

8190

3.87

2.53

1

6

Mother's education

8190

2.03

1.13

1

6

Water source

8190

1.91

0.70

1

3

Toilet type

8190

2.06

0.95

1

3

Ethnicity

8190

2.51

1.70

1

4

Province

8190

9.53

22.43

1

17

32

Appendix B: Regression analysis on child’s consumption of individual food items and nutritional status
for children less than 6 months and greater than 24 months in Lao PDR

Male
Fever
Diarrhea
Ever breastfed
Infant formula
Milk
YoghurtUnder 6 months
WHZ2
WAZ2
-0.05
-0.08
(-0.56)(-0.85)-0.12-0.02(-0.82)(-0.15)-0.21-0.26(-1.23)(-1.35)0.21
0.70*(0.65)(2.39)
0.01
0.22(0.07)(1.32)
0.38
0.52(1.25)(1.54)-1.20**-0.25(-3.06)(-0.55)HAZ2-0.07(-0.56)0.02(0.13)
0.02(0.09)
0.95*(2.15)
0.41*(2.03)
0.21(0.58)
0.84(1.64)

0.02(0.70)-0.15(-1.05)-0.00(-0.34)0.02(0.54)-0.11(-0.54)0.00(0.30)

Semi solid food
Kids
Urban
Mother’s age

0.01(0.44)-0.09(-0.61)0.01(0.91)

2 years and above
WHZ2
WAZ2
0.03
0.05+
(0.97)(1.70)-0.18***-0.13**(-3.83)(-2.84)-0.09-0.13*(-1.52)(-2.48)HAZ2-0.04(-0.96)-0.00(-0.02)-0.19**(-2.62)0.06(1.17)
0.08(1.43)
0.06(0.89)-0.06+(-1.93)-0.00(-0.29)0.06(1.25)
0.01*(2.41)

0.04(0.70)
0.14**(2.65)
0.12+(1.95)-0.03(-0.90)-0.02+(-1.90)0.07(1.44)
0.01***(5.07)

0.02(0.34)
0.15*(2.39)
0.09(1.14)
0.01(0.26)-0.02+(-1.81)0.05(0.91)
0.01***(4.83)

0.28(1.35)
0.30(1.47)
0.24(1.16)

0.42*(2.53)
0.16***(4.23)
0.11**(3.13)
0.00(.)

0.77**(2.73)
0.07(1.53)
0.00(0.09)
0.00
(.)

0.01
(0.34)
0.03(0.58)-0.02(-0.27)-0.04(-0.31)0.13(0.88)

0.07+(1.89)
0.14*(2.37)
0.10(1.25)-0.02(-0.23)0.35**(2.72)

0.11*(2.31)
0.17*(2.51)
0.16+(1.67)
0.04(0.29)
0.45**(3.26)

Child’s age
12-23
24-35
36-47
48-59
Mother’s edu (base=None)
Primary
Lower Secondary
Upper Secondary
Post sec. non tertiaryHigher0.25+(1.80)-0.02(-0.14)0.43+(1.72)-0.08(-0.25)-0.19(-0.61)0.02(0.13)
0.07(0.39)
0.17(0.85)-0.32(-1.23)-0.11(-0.32)

Wealth quintile
33

-0.15
(-0.83)0.11(0.54)-0.09(-0.32)-0.37(-1.04)-0.18(-0.35)(base=Poorest)
SecondMiddleFourthRichest-0.08(-0.52)-0.20(-1.25)0.09(0.43)-0.06(-0.23)0.16(1.02)-0.08(-0.44)0.13(0.64)
0.08(0.31)

0.21(1.07)
0.22(1.06)
0.39(1.45)
0.43(1.34)

0.01(0.13)-0.00(-0.06)-0.04(-0.68)0.16*(2.05)

0.15***(3.45)
0.21***(4.20)
0.21***(3.44)
0.55***(6.88)

0.30***(5.37)
0.36***(5.83)
0.39***(4.94)
0.71***(7.04)-0.00(-0.00)-0.19(-1.35)-0.12(-0.80)-0.20(-1.54)-0.25(-1.21)-0.00(-0.01)0.10*(2.22)
0.02(0.55)

0.01(0.15)-0.05(-1.33)-0.09(-1.62)-0.11*(-2.16)-0.07(-0.34)0.04(0.24)-0.06(-0.28)0.70*(2.16)-0.13(-0.85)0.11(0.58)

0.80*(2.03)-0.12(-0.69)0.16(0.54)-0.01(-0.10)-0.10*(-2.42)0.26***(4.22)

0.02(0.28)-0.10*(-2.47)-0.05(-0.88)0.03(0.33)-0.04(-0.79)-0.28***(-3.77)0.48*(2.31)
0.04(0.26)

0.33+(1.71)-0.11(-0.70)0.06(0.22)-0.04(-0.20)0.60***(10.01)0.10*(2.17)

0.18**(2.86)-0.12**(-2.59)-0.34***(-4.38)-0.27***(-4.98)1.05**(2.86)-0.34(-0.73)0.35(1.20)
0.12(0.42)
0.69*(2.21)
1.02***(3.51)
0.81*(2.39)
0.37(1.15)
0.47(1.31)-0.24(-0.85)0.02(0.06)-0.46(-1.41)-0.16(-0.62)-0.29(-1.06)-0.25(-0.91)-0.62*(-2.35)-0.77**(-2.74)-0.17(-0.65)-0.14(-0.59)-0.00(-0.02)-0.89+(-1.89)-0.52(-1.06)-0.50(-1.28)-0.58(-1.54)-0.97**(-2.64)-1.67***(-4.54)-1.35***(-3.47)-0.61(-1.59)-1.32**(-3.23)0.13(0.33)

0.46***(4.55)-0.29*(-2.41)
0.25**(2.67)
0.21*(2.24)
0.30**(3.20)
0.27**(3.06)
0.15(1.55)
0.31***(3.38)
0.26**(2.86)
0.24**(2.64)-0.11(-1.10)-0.36***(-3.40)0.01(0.15)
0.14(1.47)
0.17+(1.78)
0.01(0.11)-0.01(-0.10)0.02(0.17)-0.03(-0.30)0.04(0.43)-0.72***(-5.90)-0.18(-1.36)-0.30*(-2.56)-0.05(-0.46)-0.18+(-1.74)-0.34**(-3.19)-0.18(-1.54)-0.34**(-3.00)-0.40***(-3.69)-0.29**(-2.61)

Water (base=Pipe)
Protected
Unprotected

Toilet (base=Flush)
Pit latrine
Bush, bucket, other
Ethnicity (base=Lao-Tai)
Khmu
Hmong
Other

Province (base=Vientiane
Capital)Phongsaly
Luangnamtha
Oudomxay
Bokeo
Luangprabang
Huaphanh
Xayabury
Xiengkhuang
Vientiane
Borikhamxay

34

Khammuane
Savannakhet
Saravane
SekongChampasack
Attapeu

Constant
N

0.11(0.41)
0.64*(2.51)
0.20(0.57)
0.46(1.56)-0.10(-0.34)-0.05(-0.19)-0.49(-0.93)988-0.15(-0.56)-0.26(-1.17)-0.35(-1.23)-0.45+(-1.66)-0.32(-1.34)-0.22(-0.82)-1.19*(-2.40)
1038

35

-0.13
(-0.38)-0.96**(-3.01)-0.72+(-1.87)-1.13**(-3.13)-0.49(-1.55)-0.26(-0.74)-1.24+(-1.86)1003-0.05(-0.56)0.01(0.16)-0.07(-0.75)0.04(0.39)-0.04(-0.48)-0.19*(-2.07)-1.08***(-4.56)6090-0.18*-0.27*(-1.96) (-2.50)
-0.07
-0.15
(-0.84) (-1.37)-0.30** -0.40***(-3.06) (-3.56)-0.46*** -0.78***(-4.93) (-6.96)-0.15+-0.22*(-1.66) (-2.14)
-0.12
-0.04
(-1.28) (-0.32)-2.01*** -2.34***(-16.22) (-15.25)
6150
6054