Analisis Faktor Yang Mempengaruhi Penuru

Analisis Faktor yang Mempengaruhi Penurunan Angka Kematian Bayi di
Indonesia
Periode : 1991 – 2014

Daniel A. Talmera
1110512020

Ilmu Ekonomi
Universitas Andalas

Padang
Indonesia

2015

Abstract
Infant mortality rate (IMR) is an indicator of the quality of population
health. This study purposed to determine the level of infant mortality rates
and to examine the efect of macroeconomic, demographic, and
environmental policy on the health status of the children under fve years in
Indonesia. The data for this study were collected from secondary party like

WHO, World Bank, and Ministry of Health of Indonesia, the data were
analyzed statistically using Least-Square method. The result shows that
Improved Sanitation Facilities, Government Expenditure, and Women’s
Education are efectively reducing the IMR in Indonesia.

Background
Infant mortality rate is the number of infants dying before reaching one year
of age, per 1,000 live births in a given year. Infant mortality rate (IMR) is
generally regarded as an important national indicator of health because it is
particularly sensitive to general structural factors, like socio-economic
development and basic living conditions. The leading causes of infant
mortality are birth asphyxia, pneumonia, pre-term birth complications,
diarrhoea, malaria, measles and malnutrition 1. Many factors contribute to
infant mortality such as the mother's level of education, environmental
conditions, and political and medical infrastructure. Improving sanitation,
access to clean drinking water, immunization against infectious diseases,
and other public health measures could help reduce high rates of infant
mortality.
The infant mortality rate is one of three indicators used to monitor
achievements towards the Fourth Goal of the eight Millennium Development

Goals. This goal's target value is to "Reduce by two-thirds, between 1990
and 2015, the under-fve mortality rate".

1

Causes of infant mortality

Table 1

Infant Mortality Rate Indonesia 1991- 2014 (per 1000 live birth)
70
60
50
40
30
20
10
0

Source : World Bank, Publication 2014

Today, Infant mortality rate in Indonesia is tends to diminish. IMR of
Indonesia is 23 dead per 1000 in 2015, it is still high If we compare with
another Asian countries like Malaysia (6/1000 dead), Thailand (11/1000
dead), and Vietnam (19/1000 dead)2. Various eforts it has made to reduce
maternal mortality, newborns, infants and toddlers. Among others through
the placement of midwives in villages, empowering families and
communities to use the Book Maternal and Child Health (MCH handbook) and
Program Planning Labor and Prevention of Complications (P4K), and the
provision of health facilities services Obstetric Neonatal Emergency Basis
(PONED) at the health center care and Comprehensive Emergency Obstetric
Neonatal care (PONEK) at the hospital, and also Immunization program for
infant and toddler in Indonesia and improving sanitation facilities through
Settlement Sanitation Development Acceleration (PPSP) Program . In other
2

http://data.worldbank.org/indicator/SP.DYN.IMRT.IN/countries

side, the government efort to reduce infants and toddler mortality are
improving education level for women, and increasing Government

expenditure for Health3.

Literature Review
Many studied prove there are relationship between some government
policies with infant mortality rate. Cheng. et all (2012) found that Access to
improved water sources and sanitation reduces a nation’s infant mortality
rate. One study found that the infant mortality rate decreases by 1.14 deaths
per 1000 live births with increased access to an improved water source. The
rate decreases by 1.66 with increased access to improved sanitation
Fink, Günther and Hill (2011) conducted one of the most comprehensive
analyses onchild health, water, and sanitation. They merged all the DHS
datasets available for seventy countries over the period 1986 to 2007. Even
though the estimated efect of improved water and sanitation is smaller than
estimations done by other studies, they still found a positive impact in the
reduction of mortality, as well as a lower risk of diarrhea, and stunting.
However, the authors also fnd that the positive results of clean water are
more subtle and afect only children between 1 and 12 months4.
Bampoky (2013) found that there are efect of drinking water source and
sanitation on child mortality in Senegal. Household who have water piped
into the dwelling or a fush toilet increases the probability of child survival by

2.3 percentage points and 3.2 percentage points, respectively. The presence
of soap or detergent in the dwelling also increases this probability by 2.5
percentage points.
Kapoor (2010) found that Female literacy has a negative and statistically
signifcant on infant mortality. A 1% increase in female literacy is associated
with a 23% drop in IMRs on average. These results further enforce the claims
made in the existing literature, that there indeed is a strong association
between female's education and infant mortality rates.
3

http://dinkes.cirebonkab.go.id/artikel/upaya-percepatan-penurunan-angka-kematian-ibudan-bayi-baru-lahir-di-indonesia.html
4
Can clean drinking water and sanitation reducing infant mortality rate in Senegal

Data and Method

This study aims to analyze the efect of macroeconomic, demographic, and
environmental policies on the health status of the infant in Indonesia using
Least-Square method. Infant mortality can be attributed to a range of
hierarchical determinants that include health status, water and sanitation,

and socio-economic status, education factors. The data and variable which
used in this study consist of ;
(1)IMR (Infant Mortality Rate Data in Indonesia), is the number of infants
dying before reaching one year of age, per 1,000 live births in a given
year.
(2)ISF (Improved Sanitation Facilities), Access to improved sanitation
facilities refers to the percentage of the population using improved
sanitation facilities. Improved sanitation facilities are likely to ensure
hygienic separation of human excreta from human contact. They
include fush/pour fush (to piped sewer system, septic tank, pit
latrine), ventilated improved pit (VIP) latrine, pit latrine with slab, and
composting toilet.
(3)Imm (Measles Immunization), Child immunization measures the
percentage of children ages 12-23 months who received vaccinations
before 12 months or at any time before the survey. A child is
considered adequately immunized against measles after receiving one
dose of vaccine.
(4)Hex (Government Health Expenditure), Total health expenditure is the
sum of public and private health expenditure. It covers the provision of
health services (preventive and curative), family planning activities,

nutrition activities, and emergency aid designated for health but does
not include provision of water and sanitation.
(5)WE (Women Education) number of women who fnished the primary
education in Indonesia.
the data of for this study were collected from secondary party like WHO,
World Bank, and Health Statistical of Indonesia, from 1991 until 2014.

Classical Assumption5
The estimators that we create through linear regression give us a
relationship between the variables. However, performing a regression does
not automatically give us a reliable relationship between the variables. In
order to create reliable relationships, we must know the properties of the
estimators
and show that some basic assumptions about the data are
true.one must understand that having a good dataset is of enormous
importance for applied economic research.
Given
the
following
two

assumptions,
OLS
is
the Best Linear Unbiased Estimator (BLUE). This means that out of all
possible linear unbiased estimators, OLS gives the most precise estimates
of
and .
With the third assumption, OLS is the Best Unbiased Estimator (BUE), so it
even beats non-linear estimators. Also given this assumption,
is
distributed according to the Student's t-distribution about
distributed in such a way about .

, and

is

No Heteroskedasticity
The variance of the Error terms are constant.
. This means

that the variance of the error term
does not depend on the value of . If
this is the case, the error terms are called homoskedastic. To determine the
heteroskedasticity we used White Heteroskedasticity test (with hypothesis :
H0= no heteroskedasticity, H1= there heteroskedasticity) by looking at the
value of probability chi square from Obs*R-Squared, if the p-value obs * RSquare > α 0.1, then Ho is accepted and means there is no
heteroskedasticity. But if p-value obs * R-Square < α 0.1 then Ho is rejected
and means there is heteroskedasticity
No Serial Correlation
The error terms are independently distributed so that their covariance is
0.

.

To determine the serial correlation we use

5

https://en.wikibooks.org/wiki/Econometric_Theory/Assumptions_of_Classical_Linear_Regressi
on_Model

Breusch-Godfrey Serial Correlation LM Test (with hypothesis : H 0= no serial
correlation, H1= there correlation) by looking at the value of probability chi
square from Obs*R-Squared, if the p-value obs * R-Square > α 0.1, then Ho
is accepted and means there is no correlation. But if p-value obs * R-Square
< α 0.1 then Ho is rejected and means there is correlation

Normally Distributed Errors
The error terms are normally distributed.
. To determine the
Normally Distributed Error we use Histogram Normality Test (with hypothesis
: H0= no serial correlation, H1= there correlation) by looking at the value of
Jarque-Bera probability , if the Jarque-Bera probability > α 0.1, then Ho is
accepted and means the error term are normally distributed. But if JarqueBera probability < α 0.1 then Ho is rejected and means the error term are
normally distributed.

Models
To determine the contribution of the independent variables to the dependent
variable, we use this following model :
IMR = a +β1ISF + β2Imm + β3Hex + β4WE

The model is transformed into logarithmic form become following model :
LnIMR = a +β1 LnISF + β2 LnImm + β3 LnHex + β4 LnWE

Estimation Result
The result of least-square regression are presented in the equation below :

LnIMR

= 1.656858 - 1.347003 LnISF - 0.008983 LnImm
0.884148 LnWE

-

0.259110 LnHex -

t-test
3.957844)

(8.563510)
(-11.89712)
(-4.652582)

probability
(0.0008)

(0.0000)

(0.0000)

R-squared

= 0.995776

(-0.097982)
( 0.9230)

(0.0002)

Prob(F-statistic) = 0.000000

Based on the regression results in Table 4 it is known that the model used in
this study is sufciently representative, it is proved by R-squared value of
0.99 This means that about 99% of the variations in Infant Mortality Rate in
Indonesia could be attributed to the explanatory variables presented.
Then, from the equation we can found that all of independent variables have
negative relationship with dependent variable. ISF (Improved Sanitation
Facilities) has negative impact on Infant Mortality rate, where when
percentage of the population using sanitation facilities increases 1% will
reduces 1.347003% of infant mortality. t-statistic value of ISF is -11.89712,
more than t-table (1,711). It means Improved Sanitation Facilities
signifcantly reduce the Infant Mortality Rate in Indonesia.
IMM (Measles Immunization) has negative impact on Infant Mortality rate,
where when the number of infant who get infant immunization increases 1%
will reduces 0.008983% of infant mortality rate. t-statistic value of ISF is 0.097982, less than t-table (1,711). It means Measles Immunization is not
signifcantly reduces the Infant Mortality Rate in Indonesia.
Then, Hex (Government Health Expenditure) has negative impact on Infant
Mortality rate either, it 0.259110% reducing infant mortality. t-statistic value
of Hex is -3.957844, more than t-table (1,711). It means Government Health
Expenditure signifcantly reduces the Infant Mortality Rate in Indonesia.
Finally, WE (Women Education) has negative impact on Infant Mortality rate,
where when the number of women who fnish their primary education
reducing 0.884148% infant mortality. t-statistic value of WE is -4.652582,
more than t-table (1,711). It means Women Education signifcantly reduces
the Infant Mortality Rate in Indonesia.

Classical Assumptions Result

(-

The result of classical assumption test are shown in table below :
Heteroskedasticity Test
Table 2
White Heteroskedasticity Test

Heteroskedasticity Test: White
F-statistic
Obs*R-squared
Scaled explained SS

0.681263
3.010406
1.574384

Prob. F(4,19)
Prob. Chi-Square(4)
Prob. Chi-Square(4)

0.6135
0.5561
0.8134

from the table 2 we can see that the Obs*R-Squared value is 3.010406 with
probability value Chi-Square 0.5561. the value of Prob. Chi-Square more than
α 0.1 ,
it is mean
H0 is accepted and means
there was no
heteroskedasticity

Serial Correlation Test
Table 3
Breusch-Godfrey Serial Correlation LM Test

Breusch-Godfrey Serial Correlation LM Test:
F-statistic
Obs*R-squared

0.288871
0.788827

Prob. F(2,17)
Prob. Chi-Square(2)

0.7527
0.6741

from the table 3 we can see that the Obs*R-Squared value is 0.788827 with
probability value Chi-Square 0.6741. the value of Prob. Chi-Square more than
α 0.1 , it is mean H0 is accepted and means there was no serial correlation

Normality Test
Table 4
Histogram Normality Test
6

Series: Residuals
Sample 1991 2014
Observations 24

5
4
3
2
1
0
-0.04

-0.03

-0.02

-0.01

0.00

0.01

0.02

Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis

1.11e-15
0.003381
0.029427
-0.039719
0.018886
-0.600997
2.668903

Jarque-Bera
Probability

1.554413
0.459688

0.03

from the table 2 we can see that the value of Jarque-Bera Probability is
0.459688 and more than α 0.1 , it is mean H0 is accepted and means the error
term are normally distributed.

Conclusion and Policies
This study aims to analyze the efect of macroeconomic, demographic, and
environmental policies on the health status of the infant in Indonesia The
results above have very useful implications for policy formulations reducing
the infant mortality rate in Indonesia. The result shows that Improved
Sanitation, Government Health Expenditure, and Women Education are
signifcantly reduce the number of infant mortality rate in Indonesia. But
government policy for Measles Immunization is not signifcantly reduces
infant mortality rate. Indeed, it can be concluded that, socio-economic
factors play an important role in reducing IMR in Indonesia.
Improved Sanitation Facilities, and Women Education were the most
signifcant variable reducing the IMR in Indonesia were implied that
household environment are the most and the early important factor to
reduce the IMR in Indonesia. From this result we suggest policies for
improving sanitation facilities more broadly especially in Rural and densely
population area, and Then policies for improving education for women

Government Health Expenditure variable is signifcantly reduce the IMR in
Indonesia, it implied that the expense that were incurred by the Government
of Indonesia are efective reducing the IMR. From this result we suggest
policies for boots government spending for health.
Measles Immunization is not signifcantly reduces infant mortality rate. From
this result we suggest the government should promote the vaccine
movement broadly.

References

Bampoky, Chaterine. (2013 ). Can clean drinking water and sanitation
reducing infant mortality rate in Senegal. Pepperdine University,
School of Public Policy.
Benova L, Cumming O, Gordon BA, Magoma M, Campbell OM. (2014). Where
there is no toilet: water and sanitation .
Biro Pusat Statistik Indonesia (BPS). Angka Kematian Bayi Menurut Provinsi.
http://www.bps.go.id/linkTabelStatis/view/id/1270 (accessed april,
11, 2015)

Cheng JJ, Schuster-Wallace CJ, Watt S, Newbold BK, Mente A. (2012). An
ecological quantifcation of the relationships between water,
sanitation and infant, child, and maternal mortality. Environ Health;
11: 4.
Fink, G., Geunther, I., & Hill, K. (January 01, 2011). The efect of water and
sanitation on child health: evidence from the demographic and
health surveys 1986-2007. International Journal of Epidemiology, 40,
5, 1196-1204.
Kapoor, Shruti (2014). Infant Mortality Rates in India: District Level Variations
and Correlations. UC Riverside.
World Bank Development Indicators (WBI). Mortality Rate, Infant (1000).
http://data.worldbank.org/indicator/SP.DYN.IMRT.IN/countries
(accessed april, 10, 2015)
World Health Organization (WHO). Global Health Expenditure Database.
http://www.who.int/health-accounts/ghed/en/ (accessed may, 22,
2015)