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THE EFFECT OF MOTHERS’ EDUCATION ON UNDER-FIVE MORTALITY
FROM DEVELOPING COUNTRIES IN SOUTH EAST ASIA
By
Hnin Thant Phyu
THESIS
Submitted to
KDI School of Public Policy and Management
in partial fulfillment of the requirements
for the degree of
MASTER OF PUBLIC POLICY
2016
THE EFFECT OF MOTHERS’ EDUCATION ON UNDER-FIVE MORTALITY
FROM DEVELOPING COUNTRIES IN SOUTH EAST ASIA
By
Hnin Thant Phyu
THESIS
Submitted to
KDI School of Public Policy and Management
in partial fulfillment of the requirements
for the degree of
MASTER OF PUBLIC POLICY
2016
Professor Seulki Choi
THE EFFECT OF MOTHERS’ EDUCATION ON UNDER-FIVE MORTALITY
FROM DEVELOPING COUNTRIES IN SOUTH EAST ASIA
By
Hnin Thant Phyu
THESIS
Submitted to
KDI School of Public Policy and Management
in partial fulfillment of the requirements
for the degree of
MASTER OF PUBLIC POLICY
Committee in charge:
Professor Seulki CHOI, Supervisor
Professor Changyong CHOI
Professor Jaeun SHIN
Approval as of December, 2016
ABSTRACT
This paper aims to contribute for the understanding of factors measuring
mothers' educational levels on under-five children mortality from developing countries in
South East Asia. Because extremely poverty remains a huge challenge in the South East
Asia‟s developing countries. Set of factors related to socio-economic status on children
survival. My study chose under-five mortality because it can influence infant and under-
one mortality as well as Millennium Development Goals (MDG)‟s indicator. My study of
regression confirmed a strong association between mother‟s education and under-five
child mortality and remained significant after control for other factors.
The result imply that big part of the effect of mothers' education levels ( primary,
secondary and tertiary) can be substituted by investing than other factors. Investment of
girls' schooling is still one of the essential ways to contribute for child survival
improvement in the long run because the findings of this research agree that “Mother is
school”. On the other hand, the study of these areas will sustain the vital two sectors from
policy makers. This paper was established that mothers‟ education has a negative impact
on children mortality mostly; policy makers in region will have to focus their efforts on
enhancing investment of education sector and reducing under-five child mortality by
improving the performance of female education in these regions.
Copyright by
Hnin Thant Phyu (Full legal name)
2016 (Year of publication)
i
ACKNOWLEDGEMENTS
First and foremost I would like to express my earnest gratitude to my supervisors
Prof. Seulki Choi and Prof. Changyong Choi. I really appreciate for the inspiration from
their guidelines, the encouragement and constructive comments which received during
research thesis paper writing.
I am always grateful to my beloved parents and my sister because of their
unbounded help and hind encouragement. And I appreciate to Dr. Wah Wah Maung - my
Director General from Central Statistical Organization (CSO) of Myanmar for the
permission and giving opportunity to attend this school, Mr. Kitada San - Director from
SIAP in Japan who encouraged me to do this work, Professor Dr. Hnin Hnin Oo - Head of
Mathematics Department of Kyaing Tong University and Professor Dr. Basu from ISEC
of India who raised me with their hands wherever I need them.
I also thank to my mutual friends and their supports. Without all of you, my stay at
KDI cannot be. This chance would not have been created without the financial support
from KOICA. Therefore, I am highly thankful to the founders and faculty members of
KOICA-MDI Organization.
ii
Table of Contents
I. INTRODUCTION .................................................................................................... 1
1.1. Background of the Study ............................................................................................ 1
1.2. Statement of the Problems/issues ............................................................................... 1
1.3. Purpose of the Study .................................................................................................. 2
1.4. The Objectives of the study........................................................................................ 3
1.5. Research questions ..................................................................................................... 3
1.6. Study Hypothesis ....................................................................................................... 3
1.7. Structure of the Paper ................................................................................................. 4
II. LITERATURE REVIEW ........................................................................................ 5
2.1. Theoretical Background ............................................................................................. 5
2.2. The Impact of Population Size and Population Growth ............................................. 5
2.3. The Effect of Maternal Education on Child Survival ................................................ 6
2.4. The Relationship between Child Survival and a Mother‟s Health Based
Socioeconomic Status as Variables ........................................................................... 6
2.5. Long Term Effect of Maternal Education on under-five child mortality ................... 7
2.6. The Relationship between Maternal Education and Child Survival based Spatial
Demographic Analysis ............................................................................................... 8
III. THEORETICAL FRAMEWORK ....................................................................... 10
3.1. Mosley-Chen‟s Conceptual Model of Mortality…………………………………………………..10
3.2. Leroy Almendarez‟s Human Capital Theory………………………………………………………..10
IV. METHODOLOGY ................................................................................................. 12
4.1. Conceptual Framework ............................................................................................ 12
4.2. Model Specification ................................................................................................. 13
4.3. Limitations of the Study and Data Sources .............................................................. 15
4.4. Data Analysis with Panel Data ................................................................................. 15
4.4.1. Modeling under-five mortality rate on the female, primary school enrollment with
Fixed Effects: Hausman Test ................................................................................... 17
4.4.2. Results of the regressions for the female, primary school enrollment – under-five
mortality rate ............................................................................................................. 18
4.4.3. Modeling under-five mortality rate on the female, secondary school enrollment with
iii
Fixed Effects: Hausman Test ................................................................................... 20
4.4.4. Results of the regressions for the female, secondary school enrollment – under-five
mortality rate regression .......................................................................................... 21
4.4.5. Modeling under-five mortality rate on the female, tertiary school enrollment with
Fixed Effects: Hausman Test ................................................................................... 23
4.4.6. Results of the regressions for the female, tertiary school enrollment – under-five
mortality rate regression .......................................................................................... 24
4.4.7. Findings of the overall female school enrollment regression model........................ 27
V. DISCUSSION ......................................................................................................... 29
VI. CONCLUSION ....................................................................................................... 30
VII. BIBLIOGRAPHY .................................................................................................. 31
VIII. APPENDICES ........................................................................................................ 34
Appendix : RESEARCHDATA .................................................................................................... 35
iv
LIST OF GRAPH & FIGURES
1. Under five mortality graph with countries by years 2
2. Figure 1: Conceptual Framework of Child Mortality [Source: Mosley and Chen (1984)] 10
3. Figure 2: Conceptual Framework of Child Mortality 12
4. Graph: Yearly Under-five Mortality (U5MR) Graph by Country from 2000 to 2015 16
v
LIST OF TABLES
Table 1: Description of Variables 14-15
Table 2: Summary Statistics for Analysis 17
Table 3: Results of the Hausman test for the primary female on U5MR 18
Table 4: Results of the regressions for the primary female on U5MR 19
Table 5: Results of the Hausman test for the secondary female on U5MR 21
Table 6: Results of the regressions for the secondary female on U5MR 22
Table 7: Results of the Hausman test for the tertiary female on U5MR 24
Table 8: Results of the regressions for the tertiary female on U5MR 25
Table 9: Results of the regressions for the female school enrollment on U5MR 27
1
I. INTRODUCTION
1.1. Background of the Study
Although some South East Asia countries had already developed, most of South
East Asian countries were as developing countries. Moreover, extremely poverty remains
a huge challenge in the South East Asia countries as Least Development Countries (LDCs).
Therefore, income and demographic characteristics has widened continuously with
developed countries in region. R. Fuchs, W. Lutz and E. Pamuk mentioned that different
policy implications approved especially with respect to reducing child mortality in
developing countries although most studies regarded education and income as
interchangeable measures of socio-economic status. Because mothers‟ levels of schooling
and learning performance can improve the children‟ lives as well as decrease child
mortality. Although we have various kinds of indicators for child mortality, I chose under-
five mortality because it can influence infant and under-one mortality as well as
Millennium Development Goals (MDG)‟s indicator. At the same time, survival children
will be the next generation for sustainable growth. Therefore, developing countries in
South East Asia should try to reduce the children mortality for the future inclusive growth.
1.2. Statement of the Problems/issues
There is need to establish evidence of the effect of mothers‟ education on under-
five mortality in South East Asia‟s developing countries. Most of developing countries
have several challenges and many issues such as lack of infrastructure, lack of knowledge,
lack of budget and inefficiency. Moreover, absence of actual statistics, policy maker met
the failed planning for growth especially education and health sectors. For inclusive
growth, these two sectors are very fundamental sectors. Therefore, filling the gap is
beneficial to both their governments and their citizens within region. Since there is general
2
issue on the child mortality, establishing evidence on whether mothers‟ education will
contribute inclusive development efforts in the South East Asia‟s developing region. If it is
established that mothers‟ education has a negative impact on children mortality, policy
makers in region will have to focus their efforts on enhancing investment of education
sector. If the evidence proves otherwise they will think to adjust their investment policies
in the region.
1.3. Purpose of the Study
This study can support between decision makers and citizens to fulfill citizens‟
lives indirectly by government policy because child survival is often used broad indicators
for social development. Moreover, maternal education is important for health of children
3
to improve of the country's growth. Mothers' routine decisions can influence child health.
Therefore, the very research can promote socio-economic status by studying the effect of
maternal education on child survival especially under-five mortality which is to be
sustainable growth in South East Asia.
1.4. The Objectives of the study
1. To monitor the demographic variables in South East Asia developing countries on
child mortality in the age of under-five years
2. To identify the relative importance of the levels of mothers‟ education and under-
five child mortality
3. To encourage female educational levels and mothers‟ knowledge to possess the
qualified children who be the human capital in future growth
1.5. Research questions
What is the effect of mothers‟ education on the under-five mortality of developing
countries in South East Asia?
How do we reduce under-five child mortality in these regions?
1.6. Study Hypothesis
The developing countries of South East Asia are expected to be positive by the
effect of mothers‟ education on child mortality and regardless of the prevailing policy
distortions in the individual countries within the region. Nevertheless policy and
institutional quality is expected to enhance the effect of mothers‟ education. With this
argument and our research questions in mind, a hypothesis is made that under-five child
4
mortality is associated with mothers’ educational levels than other factors in South East
South East Asia’s developing countries.
1.7. Structure of the Paper
This paper is made up of the followings. Chapter 2 will be presented by literature
review and chapter 3 will discuss theoretical framework and chapter 4 is the methodology
with collecting data, specification of the conceptual models and definitions of the variables
used and interpreting. Chapter 5 will add discussion with the findings and reflection.
Finally, chapter 6 will include conclusions.
5
II. LITERATURE REVIEW
2.1. Theoretical Background
Population size and population growth of a country are thought by variables for
economic growth and that, a mother's education is essential for child survival. The study
of the determinants of child survival plays a vital role on both social and biological
variables in developing countries. The mother's quality of care is very important during
pregnancy and child birth by nutrients management. The mother is the most important
health care provider of her baby. The mother's empowerment can influence a child's
health. It is hypothesized that a mother's education is essential for child survival. The
following literature reviews support this hypothesis.
2.2. The Impact of Population Size and Population Growth
This research examined the impact of population size and population growth on
the quality of the health of mothers and their children. As indicated in a research article
by Bash (2015), census has important ramifications for many aspects of society and can
be an examined parameter in research involved in demographic analysis of the
population. Bash further investigates some limiting factors which avert population
growth and examines the differences in the results of the same period using different
models. The research design, the study area, data collection methods and instruments,
validation of the instruments, limitations and the procedure for data analysis were
discussed.
6
2.3. The Effect of Maternal Education on Child Survival
Lillard, Simon, Ueyama (2007) indicated that a mother‟s high school education
improves a mother‟s age at child‟s birth including child care use. They also discovered
suggestive evidence of a much more complex set of behaviors that are causally related to
maternal education and that likely affect child health.
This preliminary evidence suggests that the very study can strongly conclude the
child health by maternal education. The body of empirical evidence showed that not only
education can improve health but also that health can affect education.
Researchers in this field have used many methodologies; one of the methods used
is the instrument variables. With this methodology, researchers have found that, in
developing countries generally, education concerned with better child health. According to
the researcher‟s result, when women get more education, child survival (Breierova and
Duflo 2004; Blunch 2005) and children‟s height-for-age increase (Ahmad and Iqbal 2005).
Many researchers generally agree that, the relationship between education and health is a
main point input to several continuing public policies in all economy contexts.
2.4. The Relationship between Child Survival and a Mother’s Health Based
Socioeconomic Status as Variables
Usually, social science research on child mortality has engrossed on the
suggestion between socioeconomic status and levels and designs of mortality in
populations. Chen and Mosley (1984) proposed the learning of the determinants of child
survival on both social and biological variables in developing countries. The purpose of
the child survival study is to illuminate our sympathetic of the many factors involved in
the family's production of healthy children in order to deliver a foundation for framing
health policies and strategies. The strategic to the model is the sympathy of a set of
7
adjacent determinants, or intermediate variables, that directly impact the risk of morbidity
and mortality. Each of the maternal factors has been exposed to use an independent
influence on pregnancy results and infant survival through its effects on maternal health.
An important inclusion is the performants and the quality of care during pregnancy and
childbirth. The significance of this research is correlations between mortality and socio-
economic characteristics for the mortality causes. For example, income and maternal
education are two generally measured correlates (and inferred causal determinants) of
child mortality in a developing nation population. All social and economic determinants
must activate through these variables to affect child death.
2.5. Long Term Effect of Maternal Education on under-five child mortality
Child health is one of the main indicators of development of a country. Hassan
(2014) contributed to understanding the long term effect of maternal education on under-
five child mortality (U5MR).The relevance of this research stems from the fact that, asset
in girls‟ schooling is still one of the important ways to contribute not only child health
development but also growth in the long run. Hassan‟s paper examined the factors causal
child health by investigating the effects of maternal education on the event of under-five
mortality. This paper indicated the set of factors related to socio-economic status
including the use of health care service area; reproductive behaviors of women; mothers‟
empowerment; and parental employment status. These were comprised in the regressions
with the aim to catch the partial effect of maternal schooling on the incidence of under-
five mortality.
Instead, various other variables were tried for their mediation on the outcome of
maternal education on under-five mortality. These included maternal characteristics such
as family prosperity index, pre-birth interval, types of floor materials, sanitation facilities,
8
drinking water, staying health facilities, modern contraceptive use, antenatal visit, and
delivery in health conveniences. The author used the Linear Probability Model (LPM)
regressions and the pooled regressions with DHS survey for Ethiopia country which
indicated that the mean of the incidence of under-five child mortality decreased
significantly in the period of 2000 to 2011. It similarly reduced with increasing levels of
maternal education. Likewise, significantly higher mean of years of schooling was attained
for surviving mothers‟ children.
2.6. The Relationship between Maternal Education and Child Survival based
Spatial Demographic Analysis
Weeks (2001) suggested for the application of spatial analysis to demographic
research as a method to integrate and superior understand the unlike transitional
apparatuses of the whole demographic transitions especially the fertility transition in
Egypt. The authors argued in the context of a analysis of the still reasonably sparse
literature on spatial analysis in demography and then turns to the sorts of data that are
required for spatial demographic analysis; the kinds of statistical methods that are
accessible to researchers; and the approach in which Geographical Information System
(GIS) can support to integrate each of these components for the theories testing and
models building. So, demography is not only spatial but also by nature interdisciplinary.
The overall transition in population size can occur when mortality drops sooner than
fertility (the common pattern in the demographic transition) from which massive
variations follow with respect to resource utilization and allocation. This paper
hypothesis is how and why these transitions occur.
Many demographic researches, engages spatial “analysis”. Research that
incorporates spatial information recognizes that demographic behavior will differ by
9
geographic region that population characteristics and change are unlike in urban than in
rural places. The spatial analysis application to demographic research is a method of
integrating and enhanced understanding of the different transitional components of the
overall demographic transition.
Weeks also discussed the kind of data required for spatial demographic analysis,
permitting researchers to use the concepts and tools of spatial analysis to test theories
increasing out of the general framework. He also summarized the research for an upgraded
understanding of the Arab fertility transition through the challenging of explicitly spatial
hypotheses about the timing and tempo of fertility change.
Weeks‟s paper achieves with an example of this type of research, sketch upon the
author‟s study, which is expected at a better understanding of the Arab fertility transition
through the testing of explicit spatial theories about the timing and tempo of fertility
change. Definitely, this research relates GIS, remote sensing, and the relationship
between spatial statistics and the fertility transition in rural and urban Egypt.
Additionally as indicated above, the use of spatial analysis to demographic research must
be updated by accepting the different transitional components of the whole demographic
transitions especially the fertility transition.
10
III. THEORETICAL FRAMEWORK
3.1. Mosley-Chen’s Conceptual Model of Mortality
The model recommends combining social science and medical science research
methods in dealing with infant and child mortality factors in developing countries. The
following is broadcast mechanism of the model.
Figure 1: Conceptual Framework of Child Mortality [Source: Mosley and Chen (1984)]
3.2. Leroy Almendarez’s Human Capital Theory
Leroy Almendarez (2011) purposes Human Capital Theory (HTC) which
determines that investment in human capital will hint to great economic outputs but the
validity of the theory is sometimes tough to demonstrate and contradictory. At one time,
economic strength was mainly depended on real physical assets such as land, factories and
equipment. Now a days, Beckr (1993) supports modern economists appear to concur that
education and health care are the significant to improving human capital and ultimately
growing the economic outputs of the nation. He argues that Human Capital Theory (HCT)
is the most persuasive increasing the economic theory of western education, situation the
framework of government policies since the early 1960s and it is gradually seen as a key
determinant of economic performance.
Stages of
operation
Community
level: Region
Mortality
outcomes
Intervening
variables
Socio-economic
Intermediate
determinants
Independent
variables
Mothers’
Education
levels
11
Literature has indicated generally that the more educated mothers are superior for
absorbing the benefits of health infrastructure and health knowledge that are universally
accessible. Research has also indicated that giant part of maternal education can be
replaced by advancing in other factors that can improve socio-economic status, use of
health amenities, and health behaviors of women (Weeks, 2001).The extent to which
maternal education affects child survival can vary across various sectors of society and
within countries and also the direction of causation varies. The purpose of this study is to
simplify our understanding of the many factors complicated in the family's manufacture
of healthy children in order to afford a foundation for framing education policy and
strategies.
Therefore, this study builds on the strengths of the studies like that Uzma Iram
(2013) who focused a panel of world developing countries which are middle low income.
We attempt to overcome the other studies discussed in the literature review which make
general conclusions based on samples that are too broad. We believe the South East Asia‟s
developing region is reasonably homogeneous to be studied together over some time span
and make generalized conclusions. We further recognize the need to address the evidence
of the effect of mothers‟ education on under-five child mortality in South East Asia‟s
developing countries as done by Uzma Iram (2013).
12
IV. METHODOLOGY
4.1. Conceptual Framework
Although both maternal and paternal education are among the socio-economic
factors that affect child mortality, mothers' education has been one of the main factors of
child health indicators and child mortality in many studies specially. The following
diagram (Figure 2) is drawn based on the above discussion and it shows the way mothers'
education can affect under-five mortality. It shows its direct effect and indirect effect and
employs through other factors.
Figure 2: Conceptual Framework of Child Mortality
Given the nature and characteristic of the problem under investigation in this
paper, it is prudent to use linear panel data regression methods to evaluate the effect of
mothers' education on under-five child mortality in South East Asia countries. This panel
data is a dataset in which the comportment of entities observed across time. These
entities could be developing countries in South East Asia for this paper. Panel data
permits me to control for variables I cannot see or measure like cultural factors or
variables for individual heterogeneity. Furthermore, we can contain variables at different
levels of analysis for multilevel modeling in panel data. We can center on two techniques
Mothers'
Education
levels
Demographic
Factors
Socio-economic
Factors
Under-five
Mortality
13
use to analyze panel data such as Fixed Effects (FE) or Random Effect (RE) estimations
techniques for Error Component Model. We can use Fixed Effects (FE) whenever we
only engrossed for analyzing of the impact of variables that vary over time. Moreover,
FE can eliminate the effect of time-invariant characteristics thus we can measure the net
effect of the predictors on the outcome variable as well as Fixed Effects (FE) models are
considered to study the causes of changes within entity. Random Effect (RE), unlike the
fixed effects model, agrees generalizing the inferences beyond the example used in the
model. To select between fixed or random effects we can run a Hausman test where the
null hypothesis is that the ideal model is Random Effect (RE) and the alternative the
Fixed Effects (FE).
4.2. Model Specification
The baseline models specifically investigate the effect of mothers' education on
under-five child mortality in South East Asia‟s developing countries takes the form:
U5MRit = β0 + β1 Female (primary) Edu it + ø X it + ε it ………(1)
U5MRit = β0 + β1 Female (secondary) Edu it + ø X it + ε it ……(2)
U5MRit = β0 + β1 Female (tertiary) Edu it + ø X it + ε it ………(3)
For Overall Regression,
U5MRit = β0 + β1 Female (primary) Edu it + β1 Female (secondary) Edu it + β1 Female
(tertiary) Edu it + ø X it + ε it ………(4)
Where i=1...N and t=1...N
Under-five Mortality is dependent variable and independent variables are School
enrollment, tertiary, female (%), School enrollment, secondary, female (%), School
enrollment, primary, female (%), Fertility rate, total (births per woman), GDP per capita
14
(current US$), Improved water source (% of population with access), Pregnant women
receiving prenatal care (%), Births attended by skilled health staff (% of total), Health
expenditure, total (% of GDP), Improved sanitation facilities (% of population with
access), Immunization, measles (% of children ages 12-23 months). The following
definitions are variables for that study.
Main research of this paper is on the levels of school enrollment female variables.
As indicated in the hypothesis, we expect mothers‟ education levels to have negative
impact on child mortality. The theoretical basis is that female schooling levels can affect
the under-five child mortality in South East Asia‟s developing countries.
Table 1: Description of Variables
S/N
o
Variable Definition Stata
Label
1 Under-five Mortality Rate the probability per 1,000 that a baby will
pass away before reaching age
U5MR
2 School enrollment, tertiary, female The ratio of female , tertiary school
enrollment
tertiary
3 School enrollment, secondary,
female
The ratio of female , secondary school
enrollment
secondary
4 School enrollment, primary, female The ratio of female , primary school
enrollment
primary
5 Fertility rate, total (births per
woman),
The number of children that would be
born to a woman between 15 to 49 years
fertilityR
6 GDP per capita (current US$) Gross Domestic Product divided by
midyear population
gdppc
7 Improved water source (% of
population with access)
The percentage of the population using
all kinds of drinking water source
water
8 Pregnant women receiving prenatal
care (%)
The women joined at least once during
pregnancy by skilled health personnel for
prenatalCa
re
15
aims related to pregnancy.
9 Births attended by skilled health staff
(% of total)
The percentage of distributions attended
by personnel trained to care for
newborns.
Births hel
staff
10 Health expenditure, total (% of GDP) The summation of public and private
health expenditure
HealthExp
11 Improved sanitation facilities (% of
population with access)
Using improved sanitation facilities are
possible to ensure hygienic separation of
human excreta from human contact
sanitation
12 Immunization, measles (% of
children ages 12-23 months)
Children ages 12-23 months who
received immunizations before 1 year or
at any time
ImmuMea
ls
4.3. Limitations of the Study and Data Sources
Universally, mortality studies are faced with data limitations, mainly in the
developing countries in South East Asia where death is viewed as a sad affair that
respondents do not love to recall because it brings back sad memories. The data
limitations will stance a severe challenge to this study. The study therefore uses panel
data for all sample variables of South East Asia‟s developing countries from 2000 to
2015, sources from the World Development Indicators (World databank).
4.4. Data Analysis with Panel Data
According to analysis, we can see 14 variables and 272 observations and strongly
balanced among panel variables for 17 developing countries in South East Asia from 2000
to 2015 year. Although most of countries‟ graphs showed negative relationship between
under-five child mortality and year, there are still the highest mortality rates in the world.
16
Graph : Yearly Under-five Mortality (U5MR) Graph by Country from 2000 to 2015
1. Afghanistan
2. Bangladesh
3. Bhutan
4. Cambodia
5. China
6. India
7. Indonesia
8. Lao PDR
9. Mongolia
10. Myanmar
11. Nepal
12. Pakistan
13. Philippines
14. Sri Lanka
15. Vietnam
16. Timor-Leste
17. Thailand
17
Table 2: Summary Statistics for Analysis
(1) (2) (3) (4) (5)
VARIABLES N mean sd min max
Fertility Rate 255 3.074 1.363 1.447 7.496
U5MR 272 54.30 30.68 9.800 137
GDP Per Capital 259 1,534 1,422 119.9 7,925
Improved Water 272 78.40 15.56 30.30 100
Prenatal Care 98 73.95 23.62 16.10 99.50
Birth with skilled-staff 103 59.54 31.45 11.60 99.90
Health Expenditures 253 4.286 1.753 0.368 9.419
Sanitation 272 54.37 20.05 16.30 95.10
Immunization meals 253 80.65 16.21 27 99
Tertiary Female 175 20.17 18.10 0.536 75.92
Secondary Female 178 56.23 22.99 0 102.0
Primary Female 218 101.9 18.31 0 151.3
First and foremost, my paper used the summary statistics for knowing minimum and
maximum levels for variables as shown in figure.
4.4.1. Modeling under-five mortality rate on the female, primary school enrollment with
Fixed Effects: Hausman Test
Having decided to conduct panel estimation, we look another decision of whether to
estimate our model with random effects (RE) or fixed effects (FE). The general approach to
determining a more appropriate model between a fixed effects model and a random effects model
is to conduct the Hausman Test. We therefore conduct the Hausman test for the female, primary
school enrollment – under-five mortality rate model as shown in table 3 below. For Hausman test,
null hypothesis that RE is appropriate and alternative hypothesis is FE is appropriate. The test
generates a small Chi-square test statistic at 20.02 and a large p-value at 0.0103. We therefore
reject the null hypothesis that the difference in the coefficients generated by our model is
systematic and accept the alternative. We therefore proceed to estimate a fixed effects model for
the female, primary school enrollment – under-five mortality rate regression.
18
Table 3: Results of the Hausman test for the female, primary school enrollment – under-five
Mortality rate regression
(V_b-V_B is not positive definite)
Prob>chi2 = 0.0103
= 20.02
chi2(8) = (b-B)'[(V_b-V_B)^(-1)](b-B)
Test: Ho: difference in coefficients not systematic
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
b = consistent under Ho and Ha; obtained from xtreg
immumeals -.1321688 -.5133551 .3811863 .1177738
sanitation -.05376 -.2965823 .2428222 .2370414
healthexp .7869377 -.331209 1.118147 .5661843
birthshels~f -.2142654 -.2807879 .0665225 .0632162
prenatalcare -.1037497 -.0772619 -.0264878 .0582428
water -1.200191 -.4254436 -.7747479 .3407239
fertilityr -3.388727 -.5782835 -2.810444 2.990412
primary -.3198135 -.2908383 -.0289752 .0712172
fixed random Difference S.E.
(b) (B) (b-B) sqrt(diag(V_b-V_B))
Coefficients
. hausman fixed random
4.4.2. Results of the regressions for the female, primary school enrollment – under-five
mortality rate
As stated in our hypothesis, we expect the female, primary school enrollment to effect on
the under-five mortality rate by decreasing child mortality. Therefore we expect a negative
relationship between for the female, primary school enrollment – under-five mortality rate. In
Table 4 below, we will discuss the results which are estimated by regressing of under-five
mortality on mothers‟ education levels and some control variables with pooled OLS and Fixed
Effect (FE). Moreover, our interest is to see the changes in the magnitude and significance of the
coefficient for variables. Then, we can claim the changes.
19
Table 4: Results of the regressions for the female, primary school enrollment – under-five mortality rate
(1) (2)
VARIABLES OLS FE
Primary Female -0.476*** -0.315***
(0.0854) (0.117)
GDP Per Capital -0.000999 -0.00103
(0.000957) (0.000859)
Fertility Rate 3.109 -1.870
(1.908) (4.067)
Improved Water -0.171 -1.188***
(0.104) (0.379)
Prenatal Care -0.119 -0.128
(0.136) (0.140)
Birth- skilled-staff -0.198 -0.220
(0.130) (0.145)
Health Expenditures -0.558 1.219
(0.865) (1.071)
Sanitation -0.302*** 0.0764
(0.0982) (0.291)
Immunization meals -0.617*** -0.0475
(0.133) (0.196)
Constant 198.9*** 204.2***
(18.63) (29.72)
Observations 74 74
R-squared 0.941 0.891
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
In pooled OLS, a child from a mother with primary level of education is 0.476 percentage
points less likely to die under the age of five. Then, a child from good sanitation facilities is 0.302
percentage points less likely to die under the age of five. Moreover, a child from getting
immunization meals is 0.617 percentage points less likely to die under the age of five. The
statistical significance levels of these are lower than 99%.
According to FE regression results, under-five mortality rate changes or increases 0.315
percentages, when the female primary school level decreases by one unit. When the improved
20
water source decreases by one unit, under-five mortality rate will change or increases 1.188
percentages.
The findings also indicate a strong effective and negative relationship between under-five
mortality rate and them in these seventeen countries. This is also well expected and in line with
theory. Moreover, our model is very good fit because the variables have significant influences on
under-five mortality when the statistical significance levels of these are lower than 99%. We are
happy about this model. I have no doubt about the result.
We can therefore conclude that indeed female primary school enrollment by increasing
has helped in decreasing under-five mortality rate in these seventeen developing countries in
South East Asia and to this effect female primary school enrollment has been very effective
especially by model-1.
4.4.3. Modeling under-five mortality rate on the female, secondary school enrollment
with Fixed Effects: Hausman Test
For Table 5 Hausman test, null hypothesis that RE is appropriate and alternative hypothesis
is FE is appropriate. The test generates a small Chi-square test statistic at 24.29 and a large p-
value at 0.0020. We therefore reject the null hypothesis that the difference in the coefficients
generated by our model is systematic and accept the alternative. We therefore proceed to estimate
a fixed effects model for the female, secondary school enrollment – under-five mortality rate
regression.
21
Table 5: Results of the Hausman test for the female, secondary school enrollment – under-five mortality rate
regression
(V_b-V_B is not positive definite)
Prob>chi2 = 0.0020
= 24.29
chi2(8) = (b-B)'[(V_b-V_B)^(-1)](b-B)
Test: Ho: difference in coefficients not systematic
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
b = consistent under Ho and Ha; obtained from xtreg
immumeals -.1584245 -.3820334 .2236089 .1468217
sanitation -.907188 -.4654804 -.4417076 .3582411
healthexp -1.461959 -.7506087 -.7113506 .7130524
birthshels~f -.1805794 -.1328091 -.0477704 .1084893
prenatalcare .0450099 -.0575156 .1025254 .0910523
water -.7622521 -.4385035 -.3237486 .7115231
fertilityr 1.507933 .7915469 .7163861 3.264376
secondary -.0456639 -.2619816 .2163177 .0782608
fixed random Difference S.E.
(b) (B) (b-B) sqrt(diag(V_b-V_B))
Coefficients
. hausman fixed random
4.4.4. Results of the regressions for the female, secondary school enrollment – under-
five mortality rate regression
As stated in our hypothesis, we expect the female, secondary school enrollment to effect
on the under-five mortality rate by decreasing child mortality. Therefore we expect a negative
relationship between for the female, secondary school enrollment – under-five mortality rate. The
results for that are shown in Table 6 below. Moreover, our interest is to see the changes in the
magnitude and significance of the coefficient for variables. Then, we can claim the changes. As
expected, our findings indicate a negative impact of secondary school enrollment for female on
under-five child mortality in developing countries in SOUTH EAST ASIA.
22
Table 6: Results of regressions for the female, secondary school enrollment –under-five mortality
rate regression
(1) (2)
VARIABLES OLS FE
Secondary Female -0.542*** -0.0170
(0.129) (0.169)
GDP Per Capital -0.00104 -0.000208
(0.00134) (0.00151)
Fertility Rate 4.096* 2.194
(2.154) (5.680)
Improved Water -0.341** -0.807
(0.133) (0.781)
Prenatal Care 0.0506 0.0770
(0.177) (0.175)
Birth- skilled-staff -0.124 -0.193
(0.151) (0.189)
Health Expenditures -0.419 -1.449
(1.070) (1.399)
Sanitation -0.0558 -0.901**
(0.122) (0.415)
Immunization meals -0.414** -0.169
(0.169) (0.234)
Constant 146.3*** 187.6***
(17.21) (54.76)
Observations 65 65
R-squared 0.932 0.875
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
In pooled OLS, when mother with secondary level of education increases by one unit,
under-five mortality rate decreases 0.542 percentages. The statistical significance level of that is
lower than 99%. Then, a child from improved water facilities is 0.341 percentages points less
likely to die under the age of five. Moreover, a child from getting immunization meals is 0.414
percentages points less likely to die under the age of five. The statistical significance levels of
these are lower than 95%.
23
According to FE regression results, under-five mortality rate changes or increases 0.017
percentages, when coefficient of the female primary school level decreases by one unit. When of
the sanitation facilities decrease by one unit, under-five mortality rate will change or increases
0.901 percentages. The statistical significance level of that is lower than 95%.
The findings also indicate a strong effective and negative relationship between under-five
mortality rate and them in these seventeen countries. This is also well expected and in line with
theory. Moreover, our model is very good fit when probability value is significant. We are happy
about this model. I also have no doubt about the result.
We can therefore conclude that indeed female secondary school enrollment by increasing
has helped in decreasing under-five mortality rate in these seventeen developing countries in
South East Asia and to this effect female secondary school enrollment has been very effective
especially by model-2.
4.4.5. Modeling under-five mortality rate on the female, tertiary school enrollment with
Fixed Effects: Hausman Test
To determining a more appropriate model between a fixed effects model and a random
effects model by conducting Table 7 the Hausman Test, null hypothesis that RE is appropriate and
alternative hypothesis is FE is appropriate. The test generates a small Chi-square test statistic at
4.15 and a large p-value at 0.8434. We fail to reject the null hypothesis that the difference in the
coefficients generated by our model is not systematic. We therefore use to estimate a Random
effects model for the female, tertiary school enrollment – under-five mortality rate regression.
24
Table 7: Results of the Hausman test for the female, tertiary school enrollment – under-five mortality rate
Regression
Prob>chi2 = 0.8434
= 4.15
chi2(8) = (b-B)'[(V_b-V_B)^(-1)](b-B)
Test: Ho: difference in coefficients not systematic
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
b = consistent under Ho and Ha; obtained from xtreg
immumeals -.3373533 -.4157821 .0784288 .0979278
sanitation -.1329261 -.04933 -.0835961 .2540522
healthexp 1.047483 .1791319 .8683512 .5885603
birthshels~f .0257424 .0464016 -.0206592 .1087059
prenatalcare -.2707565 -.3596471 .0888907 .1007853
water -.1417429 -.3153666 .1736236 .4519853
fertilityr 9.499454 5.462412 4.037042 3.275602
tertiary -.6886475 -.5241703 -.1644772 .1376923
fixed random Difference S.E.
(b) (B) (b-B) sqrt(diag(V_b-V_B))
Coefficients
. hausman fixed random
4.4.6. Results of the regressions for the female, tertiary school enrollment – under-five
mortality rate regression
As stated in our hypothesis, we expect the female, tertiary school enrollment to
effect on the under-five mortality rate by decreasing child mortality. Therefore, we expect a
negative relationship between for the female, tertiary school enrollment – under-five mortality
rate. The results for the female, tertiary school enrollment and under-five mortality rate regression
are shown in Table 8 below. Moreover, our interest is to see the changes in the magnitude and
significance of the coefficient for variables. Then, we can claim the changes. As expected, our
findings indicate a negative impact of tertiary school enrollment for female on under-five child
mortality in developing countries in South East Asia.
25
Table 8: Results of the regressions for the female, tertiary school enrollment –under-five mortality rate
(1) (2)
VARIABLES OLS RE
Tertiary Female 0.114 -0.534***
(0.136) (0.187)
GDP Per Capital -0.00248** 0.000185
(0.00122) (0.00101)
Fertility Rate 5.055** 5.678**
(2.055) (2.730)
Improved Water 0.0444 -0.270
(0.156) (0.251)
Prenatal Care -0.493*** -0.352***
(0.163) (0.124)
Birth- skilled-staff 0.0751 0.0588
(0.144) (0.156)
Health Expenditures -1.011 0.328
(1.163) (1.045)
Sanitation -0.311** -0.117
(0.153) (0.226)
Immunization meals -0.698*** -0.420***
(0.162) (0.147)
Constant 147.5*** 129.9***
(17.76) (23.31)
Observations 69 69
R-squared 0.905
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
According to pooled OLS, when GDP per capital increases by one unit, under-five
mortality rate decreases 0.0025 percentages. Then, a child from fertility rate is 5.055 percentages
points more likely to survive under the age of five. The statistical significance levels of these are
lower than 95%. A child from getting immunization meals and prenatal care are 0.698
percentages and 0.493 percentages points less likely to die under the age of five. The statistical
significance levels of these are lower than 99%. Moreover, when sanitation facilities increase by
one unit, under-five mortality rate decreases 0.311 percentages. The statistical significance level
of that is lower than 95%.
26
In RE regression results, under-five mortality rate changes or increases 0.534 percentages,
when coefficient of the female tertiary school level decreases by one unit. Then, a child from
fertility rate is 5.678 percentages points more likely to survive under the age of five. The
statistical significance levels of these are lower than 95%. A child from getting immunization
meals and prenatal care are 0.420 percentages and 0.352 percentages points less likely to die
under the age of five. The statistical significance levels of these are lower than 99%. Moreover,
when sanitation facilities increase by one unit, under-five mortality rate decreases 0.117
percentages. The statistical significance level of that is lower than 95%.
The findings also indicate a strong effective and negative relationship between under-five
mortality rate and these variables in these seventeen countries except the fertility rate. This is also
well expected and in line with theory. Moreover, our model is very good fit when probability
values are significant. We are happy about this model and no doubt about the result.
We can therefore conclude that indeed female tertiary school enrollment by increasing has
helped in decreasing under-five mortality rate in these seventeen developing countries in South
East Asia and to this effect female tertiary school enrollment has been very effective especially by
model-3.
27
4.4.7. Findings of the overall female school enrollment regression model
Table 9: Results of the regressions for the female school enrollment for all levels on under-five
mortality rate
VARIABLES UNDER FIVE
MORTALITY
Primary Female
Secondary Female
-0.369***
(0.130)
-0.491***
(0.168)
Tertiary Female 0.106
(0.143)
GDP Per Capital -0.00105
(0.00134)
Fertility Rate 2.526
(2.401)
Improved Water -0.306*
(0.161)
Prenatal Care 0.110
(0.213)
Birth- skilled-staff -0.228
(0.177)
Health Expenditures 0.0385
(1.274)
Sanitation -0.186
(0.148)
Immunization meals -0.359*
(0.182)
Constant 183.9***
(23.47)
Observations 50
R-squared 0.948
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
According to the above overall regression results, under-five mortality rate changes or
increases 0.4 percentages and 0.5 percentages, when coefficient of the female primary and
secondary school level decreases by one unit. The statistical significance levels of these are lower
than 99%. A child from getting immunization meals and improved water are 0.4 percentages and
0.3 percentages points less likely to die under the age of five. The statistical significance levels of
28
these are lower than 90%. The findings also indicate a strong effective and negative relationship
between under-five mortality rate and these variables in these seventeen countries except the
female tertiary school level. Therefore, we can conclude that indeed female tertiary school
enrollment by increasing has helped in decreasing under-five mortality rate in these seventeen
developing countries in South East Asia and to this effect female tertiary school enrollment has
been very effective especially by model-4.
29
V. DISCUSSION
Hence above chapters present the deliveries of the study sample by designated demographic,
socio-economic and community related characteristics which could either directly and/or
indirectly affect under-five child mortality. The demographic and socio-economic characteristics
especially mothers‟ educational levels can affect child mortality where children are born and
upraised are vital and necessary to be lectured first before embarking on the study of any
component of population change (be it fertility, mortality). The study population will improve
good and clear sympathetic of the findings.
According to the reflection from my supervisors and research project evaluation result,
they evaluated my thesis that the study explores critical topic in developing countries, that is, a
relationship between mother‟s education and it impact on mortality rate. Mobilizing empirical
data, the thesis investigate a magnitude of mother‟s education, as an independent variable, on the
mortality and finds out positive relations between the two variables, which leads to a conclusion
of „mother is school‟. Overall, the study is well organized and informative for future studies.
Finally, we accept this hypothesis due to panel data analysis that under-five child
mortality is associated with mothers’ educational levels than other factors in South East
Asia’s developing countries.
30
VI. CONCLUSION
This study measures effects of mothers‟ education on under-five children mortality from
developing countries in South East Asia. In order to prove the hypothesis, this study applied
statistical analysis such as Hausman test with panel data and pooled OLS regressions by using
three models. The results of my study discovered that the effects are all statistically significant.
My study confirmed a strong association between mother‟s education and under-five child
mortality and remained significant after control for other factors. The findings of this paper
supported for an independent effect of mother education levels ( primary, secondary and tertiary)
operating through increase health knowledge such as improved water source, sanitation facilities,
immunization and etc. for under-five child mortality rate.
Therefore, the findings of this research agree that “Mother is school”. Most of developing
countries in South East Asia show higher under-five mortality rate by low-level female education.
This study can realize mothers‟ educational levels can appear to possess a stronger effect on the
under-five child mortality rate than other factors.
This also can prove the effect of mothers‟ education on the under-five mortality developing
countries in South East Asia. That is why; we can reduce under-five child mortality by improving
the performance of female education in these regions. Countries with higher level of education
can create the inclusive growth.
31
VII. BIBLIOGRAPHY
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L. (2007). Researchinto health, population and social transitions in rural South Africa: data
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Maternal Mortality 1990 to 2015 full report.PDF
34
VIII. APPENDICES
Ap
pen
dix
: R
ES
EA
RC
H D
AT
A
Co
un
try
Y
ear
ferti
lity
R
U5
MR
g
dp
pc
wa
ter
pre
na
tal
Ca
re
Bir
ths
hel
sta
ff
Hea
lth
Ex
p
san
ita
tio
n
Imm
u
Mea
ls
terti
ary
se
co
nd
ary
p
rim
ary
Afg
hanis
tan
2
00
0
7.4
96
13
7
3
0.3
3
6.9
1
2.4
23
.4
27
0
Afg
hanis
tan
2
00
1
7.3
95
13
3.8
11
9.8
99
32
2
3.9
3
7
0
0
Afg
hanis
tan
2
00
2
7.2
73
13
0.3
1
92
.15
35
3
3.8
7
.76
30
73
2
4.5
3
5
45
.574
53
Afg
hanis
tan
2
00
3
7.1
37
12
6.8
2
03
.65
1
35
.5
16
.1
14
.3
8.8
160
53
2
5.1
3
9
0.5
382
6.7
697
5
71
.034
09
Afg
hanis
tan
2
00
4
6.9
87
12
3.2
2
24
.91
47
3
7.3
8
.78
67
07
2
5.7
4
8
0.5
364
1
6.3
030
1
66
.691
46
Afg
hanis
tan
2
00
5
6.8
22
11
9.6
2
57
.17
58
3
9.1
8
.06
81
04
2
6.3
5
0
9
.56
23
2
76
.476
06
Afg
hanis
tan
2
00
6
6.6
39
11
6.3
2
80
.24
56
4
0.8
3
0.3
1
8.9
7
.43
39
72
2
6.9
5
3
1
5.7
15
63
8
2.6
03
61
Afg
hanis
tan
2
00
7
6.4
37
11
3.2
3
80
.40
1
42
.6
6.7
283
16
2
7.4
5
5
1
6.1
88
86
7
9.4
53
43
Afg
hanis
tan
2
00
8
6.2
18
11
0.4
3
84
.13
17
4
4.4
3
6
24
8.3
280
93
2
8
59
2
3.7
57
4
82
.683
23
Afg
hanis
tan
2
00
9
5.9
85
10
7.6
4
58
.95
58
4
6.2
9
.41
89
71
2
8.7
6
0
1.4
577
9
30
.122
34
8
1.0
28
99
Afg
hanis
tan
2
01
0
5.7
46
10
5
56
9.9
40
7
48
59
.6
34
.3
9.1
977
23
2
9.3
6
2
3
5.1
43
46
8
5.3
39
31
Afg
hanis
tan
2
011
5.5
06
10
2.3
6
22
.37
97
4
9.8
4
7.9
3
8.6
7
.87
19
92
2
9.9
6
4
1.8
923
3
38
.777
4
85
.526
32
Afg
hanis
tan
2
01
2
5.2
72
99
.5
69
0.8
42
6
51
.6
8.5
189
13
3
0.5
5
9
4
0.4
28
13
9
1.1
66
72
Afg
hanis
tan
2
01
3
5.0
5
96
.7
65
3.3
47
5
53
.4
8.1
348
66
3
1.1
6
0
4
0.2
05
33
9
0.7
55
72
Afg
hanis
tan
2
01
4
4.8
43
93
.9
63
3.9
47
9
55
.2
8.1
822
74
3
1.8
6
6
3
9.6
74
8
91
.757
87
Afg
hanis
tan
2
01
5
9
1.1
5
90
.26
95
5
5.3
31
.9
Ban
gla
des
h
20
00
3.1
69
88
40
6.5
31
7
76
33
.3
11
.6
2.3
267
95
4
5.4
7
4
3.5
700
4
48
.809
48
Ban
gla
des
h
20
01
3.0
69
83
.5
40
3.5
94
5
76
.7
39
.8
11
.6
2.4
699
51
4
6.5
7
7
4.4
183
5
51
.336
13
Ban
gla
des
h
20
02
2.9
71
79
40
1.7
08
2
77
.5
2.5
928
87
4
7.6
7
5
3.9
893
7
52
.969
65
Ban
gla
des
h
20
03
2.8
74
74
.8
43
4.0
46
6
78
.3
39
.7
13
.9
2.5
100
38
4
8.6
7
6
4.0
247
6
52
.723
15
Ban
gla
des
h
20
04
2.7
79
70
.7
46
2.2
74
9
79
.1
48
.7
12
.8
2.6
154
86
4
9.7
8
1
3.6
575
9
47
.453
89
Ban
gla
des
h
20
05
2.6
87
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20
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e
20
08
5.7
7
0.2
6
73
.37
45
65
.2
0.7
392
19
3
8.5
7
3
5
0.5
35
08
1
05
.59
09
Tim
or-
Lest
e
20
09
5.7
6
6.7
7
80
.26
11
66
.7
1.0
185
11
38
.8
70
13
.449
06
6
1.1
55
08
11
5.3
167
Tim
or-
Lest
e
20
10
5.6
6
3.8
8
75
.83
66
6
8.2
8
4.4
2
9.9
0
.92
23
54
3
9.1
6
6
15
.155
06
6
7.6
79
13
1
28
.41
88
Tim
or-
Lest
e
20
11
5.5
6
1.1
1
01
5.7
16
6
9.7
0
.75
74
91
3
9.5
6
2
7
1.7
13
86
1
31
.76
14
Tim
or-
Lest
e
20
12
5.3
5
8.7
11
27.1
08
71
.2
1.0
078
87
3
9.8
7
3
Tim
or-
Lest
e
20
13
5.2
5
6.5
1117
.731
71
.4
1.2
867
72
4
0.1
7
0
7
2.8
07
64
1
33
.75
59
Tim
or-
Lest
e
20
14
5.1
5
4.5
11
31.2
31
71
.7
1.4
753
03
4
0.4
7
4
7
5.9
73
04
1
36
.04
7
Tim
or-
Lest
e
20
15
5
2.6
11
34.4
26
71
.9
4
0.6
Thai
land
2
00
0
1.6
71
22
.5
20
16
.04
1
91
.9
91
.8
99
.3
3.7
919
11
91
.3
94
38
.297
42
96
.437
2
Thai
land
2
00
1
1.6
41
21
.5
18
96
.97
1
92
.4
3.7
384
13
9
1.7
9
4
41
.734
05
6
2.0
61
91
9
6.2
53
63
Thai
land
2
00
2
1.6
16
20
.5
20
93
.97
9
92
.9
4.6
807
65
9
2.1
9
4
42
.238
62
6
4.6
72
83
9
7.1
00
83
Thai
land
2
00
3
1.5
95
19
.6
23
49
.38
5
93
.3
4.7
646
04
9
2.5
9
6
43
.921
73
Thai
land
2
00
4
1.5
8
18
.7
26
43
.47
9
93
.8
94
.3
4
.56
71
02
9
2.8
9
6
45
.707
48
6
9.2
97
44
9
8.0
69
05
Thai
land
2
00
5
1.5
68
17
.8
28
74
.38
6
94
.3
4.6
447
9
93
.2
96
47
.155
02
7
3.5
84
01
9
6.9
20
7
Thai
land
2
00
6
1.5
61
17
33
51
.118
94
.7
97
.8
97
.3
4.8
577
4
93
.5
96
45
.982
69
7
4.2
70
11
9
6.0
70
91
Thai
land
2
00
7
1.5
57
16
.3
39
62
.75
95
.2
5.4
525
9
93
.5
96
53
.344
26
8
0.4
66
56
9
6.3
25
04
Thai
land
2
00
8
1.5
53
15
.6
43
84
.78
3
95
.6
5.6
607
5
93
.5
98
52
.285
25
8
0.9
86
46
9
6.8
39
68
Thai
land
2
00
9
1.5
51
15
42
31
.14
96
99
.1
99
.5
5.7
952
63
9
3.4
9
8
53
.458
44
8
3.7
12
82
9
6.4
65
72
Thai
land
2
01
0
1.5
47
14
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5111
.909
96
.4
5.4
092
97
9
3.3
9
8
56
.333
25
8
6.1
15
71
9
5.0
31
39
Thai
land
2
011
1.5
42
14
55
39
.49
4
96
.8
5.9
142
57
9
3.3
9
8
58
.946
93
8
9.9
43
1
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.520
3
Thai
land
2
01
2
1.5
34
13
.5
59
15
.22
1
97
.1
98
.1
99
.6
6.1
585
45
9
3.2
9
8
58
.387
31
8
9.4
96
35
9
6.3
75
69
Thai
land
2
01
3
1.5
24
13
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62
25
.05
2
97
.5
6.1
752
15
9
3.1
9
9
58
.857
57
8
9.3
44
88
9
6.8
87
68
Thai
land
2
01
4
1.5
12
12
.6
59
69
.94
97
.8
6.5
294
33
9
3
99
Thai
land
2
01
5
1
2.3
5
81
6.4
41
9
7.8
93
45