2012 Cambridge Business & Economics Conference
ISBN : 9780974211428
THE EFFECT OF HUMAN HEALTH ON SUSTAINABLE
DEVELOPMENT IN INDIA: THE STATE LEVEL ANALYSIS@
RUDRA P. PRADHAN1 AND TAPAN P. BAGCHI2
ABSTRACT
Health is a crucial aspect of human capital and is very critical ingredients to
sustainable development. World Bank (1993) provides four possible ways on the
association between health and sustainable development. Good health stands for higher
labour productivity, increases the level of well being and hence, maintains sustainable
development. The lack of same not only affects sustainable growth but also dilute the
environment too. However, the status of health depends upon the health spending in the
economy. Spending on health certainly leads to formation of human capital and that
provides significant contribution to sustainable development. Keeping in above backdrop,
the paper makes an attempt to study the nexus between government spending on health,
health status and economic growth in India during 1980-2009. It finds that health
infrastructure has substantial impact on health status and determines economic growth in
the Indian economy. The paper accordingly suggests that government spending on health
should be gear up properly to maintain better health status in the country and that will
produce sustainable economic growth at the disaggregate level in India.
Keywords: Human health, sustainable development, India
=============================================================
@
: Paper to be included in “Cambridge Business and Economics Conference”, during
June27-28, 2012.
1
: Assistant Professor, Vinod Gupta School of Management, Indian Institute of
Technology Kharagpur, India. Email: rudrap@vgsom.iitkgp.ernet.in
2: Director, Narsee Monjee Institute of Management Studies (Mumbai) Shirpur Campus,
Dhule 425405, India. Email: tapan.bagchi@nmims.edu
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2012 Cambridge Business & Economics Conference
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THE EFFECT OF HUMAN HEALTH ON SUSTAINABLE
DEVELOPMENT IN INDIA: THE STATE LEVEL ANALYSIS
1. INTRODUCTION
The concept of sustainable development summarizes the challenges that the world is
facing- to manage a global social and economic development that neither degrades the
ecological systems nor exhausts natural resources (WCED, 1987). Infrastructure systems,
hereafter referred to as infrasystems (Kaijser 1994), play a key role in this problem area.
The development of infrasystems has in many ways made everyday life easier (Jonsson,
2005). Infrasystems, in general, represents the set of facilities without which no activities
can be undertaken in the society. Its installations do not produce goods and services
directly but essential inputs for all other socio-economic activities (Sanchez-Robles,
1998; Canning et al., 1994). Infrastructure development has a key role to play in both
sustainable economic growth (Sengupta, 1998; Mehrotra and Jolly, 1998) and poverty
alleviation. Hence, infrasystems constitute the wheels, if not the engines of sustainable
development (WDR, 1994). Infrastructure, as a whole, divided into two groups such as
economic and social. Health is one of the crucial components of social infrastructure. It
is the key to formation of human capital and is very ingredients to sustainable
development.
World Bank (1993) provides four possible ways on the association
between health and sustainable development. Good health stands for higher labour
productivity, increases the level of well being and hence, maintains sustainable
development. On the contrary, poor health stands for lower labour productivity and level
of wellbeing and hence, lowers economic growth (Pradhan et al., 2011; Behrman and
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Deolalikar, 1988). For instance, infection and malnutrition among the children cost a long
shadow on their lives and their future productivity and that certainly contributes to lower
economic growth. Many mothers also die due to pregnancy and child birth related
reasons.
The problem is very acute in the country like India. India’s cross-country differences
in life expectancy at birth are largely due to differences in infant and child mortality rates
(Schultz, 1999). The health insurance in India is not encouraging. It is among 10% of the
people and that to mainly government employs and people working in the formal sector.
The ill-health can push them even initially non-poor households into chronic poverty.
Provision of immunization and parental care can reduce the current levels of very high
infant, child and maternal mortality and morbidity quickly. But these services are very
costly and available at the limited centres only. The status of health infrastructure is also
not good in the country. Hence, the health outcomes are at the low level, particularly in
contrast to other emerging countries like China, Sri Lanka and Mexico (see Table 1). It is
true that creating the decisive level of health infrastructure in the country is very
fundamental for attaining the important societal goals of economic growth, equity,
efficiency and poverty alleviation. However, the status of health infrastructure depends
upon the health spending in the economy. Spending on health certainly leads to formation
of human capital and that provides significant contribution to sustainable development.
Keeping in above backdrop, the paper makes an attempt to investigate the followings:
First, identify the health indicators, both at the input levels (like hospitals, PHCs, CHCs,
etc.) and output levels (like CBR, CDR, IMR, LER, etc.) and then describes their trends
in India, both in time and space. Second, workout the interface between health inputs and
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health outputs and then studied the impact of health outputs to economic growth. Third,
work out the interface between government spending on health, health status and
economic growth in India. Fourth, investigate the nexus between health spending, health
and economic growth at the state level.
The rest of the paper is organized into four different sections. Section II describes the
status of health infrastructure in India. Section III investigates the interface between
health infrastructure and economic growth. Section IV works out the interface between
health spending, health infrastructure and economic growth. Section V follows summary
and conclusion.
II. THE STATUS OF HEALTH INFRASTRUCTURE IN INDIA
In this section, we first identify the major indicators of health infrastructure, both at
the input and output level, and then describe their trends in the Indian economy. The
Table 1 presents the detail classification of health infrastructure, both at the input levels
and output levels.
Health Infrastructural Inputs
Health Infrastructural Output
Indicators
 Crude Birth Rate (CBR)
 Hospitals
 Crude Death Rate (CDR)
 Beds
 Infant Mortality Rate (IMR)
 PHCs
Human Resources
 Doctors
 Dentist
 Nurses
 Life Expectancy Rate (LER)
 Sub-centers
 Couple Protection Rate (CPR)
 CHCs
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The analysis of this part deals with the formulation of composite index by the
application of principal component analysis (PCA). The method of PCA is a special case
of more general method of factor analysis. Its aim is to construct, out of set of variables,
Xi’s (I= 1, 2, …, n), a new set of variables (Pi) called principal components, which are
linear combination of the X’s (Koutsoyiannis, 1978). Mathematically, it could be
presented as follows:
P1 = a11X1 +…………….. + a1n Xn
…………………….…………………
Pm = am1 X1 +………….. + amn Xn
………. (1)
Where, aij’s are called loadings of the factors (principal component). These are
chosen in such a way that the constructed principal components satisfy two conditions:
(a) the principal components are uncorrelated and (b) the first principal component P 1
absorbs and accounts for the maximum possible proportion of the total variation in the set
X’s, the second principal component absorbs the maximum of the remaining variation in
the X’s (after allowing for the variation accounted for by the first principal component)
and so on. In this process, the data matrix can be transformed to a new set of uncorrelated
variables (principal components) that accounts as much of the variation as possible in
descending order. The method of PCA can be applied by using the original variables or
the standardized variables. By applying standardized methods, the above expression can
be additionally represented as:
Pi 
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m
n
i 1
j 1
 a
ij
X ij
x
………. (2)
ij
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The present study worked out the composite index by the help of standardized
method only, as this process involves unit free measurement. It applies first principal
component analysis to capture the maximum proportion of the variance in the original
variables. The empirical investigation has been carried out in India during 1980 to 2009.
The data used under the study are secondary in nature and have been collected from
various sources namely National Accounts Statistics, Central Statistical Organization,
New Delhi; Health Monitor in India, FRHS, New Delhi; Health Information, Ministry of
Health and Family Welfare, Government of India, New Delhi; Statistical Yearbook for
Asia and the Pacific, World Health Organization. The estimated results of this section are
reported in Figure 1.
It is to be noted that with more than one billion people, India is identified as the
second most populous country in the world accounting for 17% of world’s population.
The country is recently recognized as the world’s fastest growing economies with an
average growth rate of 8% over the last few years. It has emerged as global player in
several areas, including information technology, business process outsourcing,
telecommunications and pharmaceuticals (WHO, 2006-11). At the same time, the country
is facing several challenges in the areas of socio-economic development, socio-economic
infrastructure, poverty, equity, etc. In fact, the existing growth cannot be sustainable, if
there is serious other problems like poverty, inequality, etc. But in this section, we just
highlight the present status of health infrastructure only. This is because it is a key to
sustainable economic growth.
Starting with health input infrastructure, it is observed that India’s progress on overall
infrastructure (in the form of composite index) has been increasing over the years. The
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composite input infrastructure index (CIII), which is an average of beds, PHCs, sub
centers, CHCs, hospital, dentists, nurses and doctors, is of 1.6 in 1980 and has been
increased to 2.97 in 1990 and then 4.92 in 2009 (See Figure 1). Coming to health output
infrastructure, it is also observed that India’s progress is well articulated. The composite
output infrastructure index (COII), which is average of CBR, CDR, IMR, LE and CPR, is
of 14.99 in 1980 and has been decreased to 8.85 in 1990 and then 2.19 in 2009 (see
Figure 1). But the availability and increase of infrastructure is not uniform across the
states of India. It is substantially high in some states, while it is low in some other states.
This will be investigated in the subsequent section.
Figure 1: Status of Health Infrastructural Outputs in India
CIII
CIOI
16
14
CIII & CIOI
12
10
8
6
4
2010
2005
2000
1995
1990
1985
1980
2
Note: CIII: Composite Index of Input Infrastructure; CIOI: Composite Index of
Output Infrastructure
Source: Authors Calculation
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III. THE INTERFACE BETWEEN HEALTH INFRASTRUCTURE AND
ECONOMIC GROWTH
It is considerably true that health infrastructure and economic growth are very
interdependent to each other. On the one hand, economic growth leads people to live
better, longer lives and good health. There are two possible ways we can justify the same.
First, economic growth means increasing per capita income and part of this increased
income can be translated into the consumption of higher quantity and better quality
nutrients. Again through nutrition, health is measured by life expectancy responds to
increase in income (Fogel, 1997). Second, economic growth is fuelled by technological
progress and part of this progress is reflected in improvement in medical science (Rosen,
1993; Morand, 2005). On the other hand, the status of health can also affect economic
growth through various channels. For instance, when health improves, the country can
produce more output with any given combination of skills, physical capital and
technological knowledge (Bloom et al., 2001; Mankiw et al., 1992; Barro, 1991). This is
otherwise called as the effect of ‘human capital’ on economic growth. Moreover, the
provision of health infrastructure not only brings sustainable economic growth but also
assist the poor to release resources for other investments, such as education, as a means to
escape poverty (Halder, 2008). That means it can also solve the problem of poverty in the
economy. However, in reality, the health status is not up to the mark in most of the
countries in the world, including India. This may be due to low government spending on
health infrastructure. So we first trace the interface between health infrastructure and
economic growth and then integrate the same with government spending on health. In
this section, an attempt is made to investigate the link between infrastructural inputs and
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outputs and their linkage with economic growth. The detail of empirical investigation of
this part is described in the Figure 2.
Figure 2: A Network of Health Infrastructure with Economic Growth
Health Inputs
Health Outputs
Economic Growth
Hospitals
CBR
Beds
CDR
PHCs
IMR
GDP
Sub-centres
LE
CHCs
CPR
Doctors
Dentist
Nurses
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We use bivariate simple regression technique for establishing the link between health
infrastructural inputs and outputs. However, we use Granger causality test for tracing the
impact of health infrastructural output on economic growth. In other words, we trace the
long run equilibrium between health infrastructural outputs and economic growth in the
Indian economy. The Error Correction Model (ECM) has been used for the same, which
can be presented as follows:
p
q
r
s
j 1
j 1
j 1
Z t  1   i GDPt  i    j CBRt  j   r CDR j    j IMR 
i 1
m
 LE
j 1
j
t j
n
………. (3)
   j CPRt  j  1ECt 1  
j 1
Where, Z = {GDP, CBR, CDR, IMR, LE, CPR), Δ is the first difference operator and
ECt 1 is the lagged stationary residuals from the cointegration equation. The test is to
reject the null hypothesis of non-causality between health and economic growth against
an alternative hypothesis of causality between the two. However, the first and foremost
condition of ECM is to test the unit root and cointegration (Engel and Granger, 1987).
The Augmented Dickey Fuller (Dickey and Fuller, 1981) unit root test has been applied
to check the unit root (stationarity) of the variables. The test follows the estimation of the
following model.
p
Yt  1   2Yt 1    i Yt i   t
………. (4)
i 1
Where Y is the variable of choice; ∆ is the first- difference operator; αi (for i = 1 & 2)
and βi (for i = 1, 2… p) are constant parameters; and εt is a stationary stochastic process.
The null hypothesis are H0: α2 = 0 against H0: α2 ≠ 0 for equation 1 and H0: η1 = 0 against
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H0: η1 ≠ 0 for equation 2 respectively. Let‘d’ denotes the number of times that a variable
needs to be differenced in order to reach the stationarity. In this case, such a variable is
said to be integrated of order‘d’ and denoted by I (d).
The second condition of ECM is to know the presence of cointegration. The aim of
cointegration is to know the long run movements of one variable relative to others. The
Johansen and Juselius (1990) test is used to study the long run equilibrium relationship
between the variables. The test is meant for two statistics: trace (Tr) statistics and the
maximum eigenvalue (max) statistics. The estimation procedures of these two statistics
are as follows:
Let Xt be a (n X 1) vector of variables with time period t and they follow I (1) process.
The investigation of number of cointegrating vector involves the estimation of
unrestricted Vector Auto-regression model.
p 1
X t  A0   X t  p   Ai X t i   t
………. (5)
i 1
Where,  is impact matrix and contains information about long run relationships
between variables in the data vector. If the rank of  (say r) is equal to zero, the impact
matrix is a null vector. If  has full rank, n, then the vector process xt is stationary. If 0
< r < n, then there exists r cointegrating vectors. Here, the impact matrix is
   
………. (6)
Where, both  and  are (n x r) matrices. The cointegrating vectors  have the
property that  X t is stationary [I (0)] even though Xt is non-stationary [I (1)].
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The cointegrating rank, r, can be formally tested with two statistics. The test statistic
for the null hypothesis that there are at most r cointegrating vectors is the trace test and is
computed as:
Trace  T
 Log 1  ˆ 
n
i
i  r 1
………. (7)
Where ˆr 1 , ….. ̂ n are (n-r) smallest estimated eigenvalues. The test statistic for the
null hypothesis of r cointegrating vectors against the alternative of r + 1 cointegrating
vectors is the maximum eigenvalue test and is given by

max  TLog 1  ˆr 1

………. (8)
Here the null hypothesis of r cointegrating vectors is tested against an alternative
hypothesis of r +1 cointegrating vectors. That means the null hypothesis r = 0 is tested
against r = 1 and r =1 is tested against r = 2 and so on. It is well known that the
cointegration tests are very sensitive to choice of lag length. The Schwarz Bayesian
Criterion (SBC) is used to select the number of lags required in the cointegration test. The
empirical investigation has been carried out in India, particularly during 1980 to 2009.
The estimated results and its discussion are represented in the subsequent section.
The results confirm that health infrastructural inputs (hospitals, beds, PHCs, subcentres, CHCs, doctors, dentist and nurses) have significant impact on health
infrastructural outputs. The crude birth rate is mostly influenced by hospitals, CHCs and
dentists, while crude death rate is mostly influenced by beds and CHCs. On the contrary,
infant mortality rate is mostly influenced by beds and PHCs, while life expectancy at
birth is mostly influenced by hospitals and beds and couple protection rate is substantially
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influenced by sub-centres and doctors (See Table 2). In short, it can be concluded that
health infrastructural outputs are primarily determined by health infrastructural inputs in
the Indian economy.
In the second part, we investigate the Granger causality between health infrastructural
outputs and economic growth. The first and foremost condition of Granger causality is to
know the existence of unit root and cointegration. The Augmented Dickey Fuller test has
been applied to know the stationarity of the time series variables. The estimated results
are reported in Table 3. The results indicate that all the variables are integrated of order
one, i.e. I (I), and confirms the possibility of cointegration among them. So there is need
to examine the long run equilibrium relationship between health indicators and economic
growth. The Johnasen’s Maximum Likelihood cointegrating test has been applied to
know the existence of long run equilibrium between the variables under consideration.
The estimated results, particularly eigenvalues and trace statistics, are presented in Table
4. The results confirmed that health indicators and economic growth are cointegrated
with each other, indicating the presence of long run equilibrium relationship between
them during the periods under the present study.
Since cointegration relationship is found between health infrastructure output and
economic growth, Granger causality test can be applied to know the direction of
causality. The Granger (1988) causality forecasts that there should be at least one
direction of causality between the two variables, if they are cointegrated. Accordingly,
the causality model, which has been described above has been applied here and that has
been tested by F-statistics. The estimated results are reported in Table 5. The results
confirmed that there is presence of uni-directional causality from crude birth rate to
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economic growth (CBR => GDP), crude death rate to economic growth (CDR => GDP),
to life expectancy at birth to economic growth (GDP => LE), crude death rate to crude
birth rate (CDR => CBR), crude death rate to infant mortality rate (CDR => IMR), crude
birth rate to couple protection rate (CBR => CPR), and life expectancy at birth to couple
protection rate (LE => CPR). There is also the presence of bidirectional causality
between life expectance and economic growth (LE < => GDP) and between couple
protection rate and economic growth (CPR <=> GDP). On the contrary, the paper finds
no causality from infant mortality to economic growth (IMR  GDP), infant mortality
rate to crude birth rate (IMR  CBR), life expectancy to crude birth rate (LE  CBR), life
expectancy to crude death rate (LE  CDR ), infant mortality to life expectance (IMR 
LE), and infant mortality to couple protection rate (IMR  CPR).
IV.
INTERFACE
BETWEEN
HEALTH
SPENDING,
HEALTH
INFRASTRUCTURE AND GROWTH
This section presents the nexus between health spending, infrastructural outputs and
economic growth. It deals with three core issues: does growth causes health status? Does
health spending causes health status? Does growth causes health spending? We use ECM
approach to investigate the same. This is as follows:
p
q
r
s
i 1
j 1
j 1
j 1
Z t  1    i GDPt  i    j CBRt  j    r CDR j    j IMR 
m
 LE
j 1
j
t j
n
o
j 1
j 1
………. (9)
   j CPRt  j   j GSH t  j  1 ECt 1  
Where, Z = {GDP, CBR, CDR, IMR, LE, CPR, GSH), Δ is the first difference
operator and ECt 1 is the lagged stationary residuals from the cointegration equation.
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This test is also followed by the unit root test and cointegration test, which have already
been discussed in the previous section.
The estimated results of unit root are reported in Table 3, while the estimated results
of cointegration are reported in Table 6. The results indicate that variables are I (I) and
hence, there is possibility of cointegration among them. The cointegration test, which is
followed by eigenvalues and trace statistics, also confirmed that health spending, health
infrastructure and economic growth are cointegrated, indicating the presence of long run
equilibrium relationship among them. The Granger causality test is then finally applied to
know the direction of causality. The estimated results are reported in Table 7. The results
confirmed that there is presence of uni-directional causality from crude death rate to
economic growth (CDR => GDP), life expectancy to economic growth (LE => GDP), life
expectancy to government spending on health (LE => GSH), couple protection rate on
government spending on health (CPR => GSH), crude death rate on infant mortality rate
(CDR => IMR), and life expectancy on couple protection rate (LE => CPR). It also finds
the existence of bidirectional causality between government spending on health and
economic growth (GSH <=> GDP) and couple protection rate and economic growth
(CPR <=> GDP). In rest of other situations, we do not find any causality. In short, it is
the government spending on health causes economic growth and that can substantially
affect the health infrastructure in the economy. The reverse can also true but the present
data set up does not give any clear indication for the same. This may be due to low
sample size in the present empirical investigation. The results can be changed, if the
sample size will be substantially high. So it can be considered as one of the limitations of
this paper.
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V.
INTERFACE
BETWEEN
ISBN : 9780974211428
HEALTH
SPENDING,
HEALTH
INFRASTRUCTURE AND GROWTH IN THE STATE LEVEL ANALYSIS
In the previous section, we discuss the interface between health spending, health
status and economic growth in India at the aggregate level during the period 1980-2009.
In this section, we like to investigate the same at the state level during the period 19802009. We use three variables in this section, such as health spending, infant mortality rate
(as a proxy to health infrastructural output) and per capita net state domestic product (as a
proxy to economic growth). The periods and indicators are choosen on the basis of data
availability only and these are collected from RBI Bulletin, Reserve Bank of India,
Mumbai, Central Statistical Organization, Government of India and sample registration
system, Registrar General, Government of India. There are two parts of discussion here.
First, the regional disparities among the 15 major states of India; second, the nexus
between health spending, health status and economic growth. For the regional disparities,
an achievement index has been prepared with respect to health spending, health
infrastructure and economic growth. The results are presented in Figure 3.
It is observed that, health expenditure is highest in Punjab and Kerala, while it is
lowest in Bihar and Uttar Pradesh. This is true in both the periods, such as 1980 and
2006.The achievement index of health status is highest in Kerala in both periods, while it
is lowest in Uttar Pradesh and Madhya Pradesh in 1980 and 2006 respectively. Coming to
economic growth, Punjab and Maharashtra occupies the first place, while Bihar stays at
the lowest level. This is true for both the periods under study. But overall, most of the
states have experienced spectacular growth in health spending, health status and
economic growth. That means there is nexus between health spending, health status and
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economic growth. This could be first verified by simple rank correlation analysis and
then examined by Granger causality test.
Figure 3: The Status of Health Spending, Health and Economic Growth
1.0
Health Spending in 1980
Health Spending in 2006
Health Status in 1980
Health Status in 2006
Economic Growth in 1980
Economic Growth in 2006
Achievement Index
0.8
0.6
0.4
0.2
B
ih
a
G r
uj
ar
H at
ar
ya
K na
ar
na
ta
ka
M
ad K
hy er
a ala
P
ra
M de
ah s
ar h
as
ht
ra
O
ris
sa
P
un
R jab
aj
as
Ta tha
m n
U il N
tta a
r P du
r
W ade
es s
tB h
en
ga
l
A
nd S
hr tat
a
e
P s
ra
de
sh
A
ss
am
0.0
Source: Authors Calculation
The results suggest that there is significant positive association between health
spending, health status and economic growth in India during the periods 1980 and 2006
(see Table 8). But correlation analysis could not indicate the direction of causality. So we
deploy Granger causality to detect the direction of causality between health spending,
health status and economic growth. It is to be noted that the prime condition of Granger
causality is to know the stationarity and cointegration among the variables. The estimated
results indicate that all the variables are 1 (1) and cointegrated. So VECM is applied to
detect the direction of Granger causality. The results of Granger Causality are presented
in Table 9. It finds that there is bidirectional causality between health spending and
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economic growth in (GSH <=> SDP) all the states, except Andhra Pradesh, Madhya
Pradesh and Uttar Pradesh, during the period 1980 to 2006. Coming to the nexus between
growth and health status, we find bidirectional causality between growth and health (SDP
<=> IMR) in the states of Madhya Pradesh, Punjab, Karnataka, Uttar Pradesh and West
Bengal. The unidirectional causality is also detected from economic growth to health
(SDP => IMR) in the states of Andhra Pradesh and Maharashtra, while the reverse
causality (IMR => SDP) is found in the states of Rajasthan, Haryana and Gujarat.
Coming to health and health expenditure, we find health expenditure affects health (GSH
=> IMR) in Bihar, Madhya Pradesh, Rajasthan, Haryana, Karnataka, Uttar Pradesh and
West Bengal. Please note that the states like Bihar, Madhya Pradesh, Rajasthan and Uttar
Pradesh are socio-economically backward. Thus, we can conclude that government
spending on health is largely affected the health status in these states. The outcomes
could be different, if the private spending on health infrastructure and other variables are
added into this analysis. This is, however, beyond the scope of this study and mostly, due
to non-availability of data only.
VI. SUMMARY AND CONCLUSIONS
The paper has attempted to study the nexus between health infrastructure, health
spending and economic growth in India during 1980-2009. The main findings of this
study are as follows:
 The simple regression analysis confirms that health infrastructural inputs have
substantial impact on health infrastructural outputs such as crude birth rate,
crude death rate, infant mortality rate, life expectancy at birth and couple
protection rate. The most important health infrastructural inputs that
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determined the above outcomes are CHCs, PHCs, hospital, beds, doctors and
sub-centres.
 The unit root test confirmed that both health indicators, health spending and
economic growth are non-stationary at the level data but found stationary at
the first difference, indicating that they are integrated of order one.
 The cointegration test confirmed that economic growth, health spending and
health infrastructure are cointegrated, indicating an existence of long run
equilibrium relationship among them.
 The Granger causality test finally confirmed that there is presence of unidirectional causality from economic growth to crude birth rate, crude death
rate, infant mortality rate and life expectancy at birth. It also finds the
presence of bidirectional causality between life expectance and economic
growth and between couple protection rate and economic growth.
 Again with the inclusion of government spending on health, it finds the
existence of bidirectional causality between government spending on health
and economic growth and couple protection rate and economic growth. So it
can be concluded that government spending on health has substantial impact
on the nexus between health and economic growth. This supports the present
hypothesis of this paper.
 The findings are somewhat very similar in the state level analysis. We find the
existence of bidirectional causality between government spending on health
and economic growth in all the states except Andhra Pradesh, Madhya
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Pradesh and Uttar Pradesh. There is also bidirectional causality between
economic growth and health status in the states of Karnataka, Madhya
Pradesh, Uttar Pradesh and West Bengal. Besides, there is substantial
unidirectional causality from health expenditure to health status in the states
of Bihar, Madhya Pradesh, Rajasthan, Haryana, Karnataka, Uttar Pradesh and
West Bengal.
The paper accordingly suggests that government spending on health should be
gear up properly to maintain better health status in the country and that will produce
sustainable economic growth at the disaggregate level in India. The lack of same not
only affects health status of the country but also affects economic growth in the long
run. So the piece-meal approach to such a vital objective is of serious consequences.
What is urgently required is to follow an appropriate policy measures so that we can
address these problems in a right earnest. It is not a daunting task, if there is adequate
political will in the country.
===== ***** =====
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Table 1: Status of Health Infrastructure in Asian Emerging Countries
CBR
CDR
LE
IMR
Beds
Doctors Nurses
Exp
China
13
7
73
19
22
1.4
10
4.7
India
23
9
64
56
7
0.6
13
5.0
Malaysia
21
4
74
10
19
7
18
4.2
Sri Lanka
15
7
72
26
29
0.6
17
4.1
Philippines 19
5
72
23
13
1.1
61
3.2
Note: CBR: Crude Birth Rate; CDR: Crude Death Rate; LE: Life Expectancy at Birth;
IMR: Infant Mortality Rate; and Exp: Expenditure on health as a percentage of
GDP.
Source: Statistical Yearbook for Asia and the Pacific, 2008; UK India Business Council,
2009.
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Table 2: Regression Results between Health Infrastructural Inputs and Outputs
CBR
CDR
IMR
LE
CPR
Hospitals
-0.98
-0.18
0.42
0.71
-0.10
Beds
-0.68
-0.79
-1.37
1.18
1.13
PHCs
0.17
-0.33
-1.64
-0.31
-0.29
Sub-centres
0.04
-1.36
-1.18
-0.67
1.22
CHCs
-2.78*
-0.22
-0.46
0.03
0.35
Doctors
-0.61
-0.30
-0.98
-0.24
0.55
Dentist
-0.16
-0.79
-0.87
0.85
-0.69
Nurses
-0.75
-0.44
-0.72
0.79
-0.16
R2
99.5
97.5
98.7
97.1
93.6
Stepwise Results
Hospitals
-1.72**
3.92*
-2.46*
Beds
-15.17*
2.60*
-4.16*
PHCs
3.52*
Sub-centres
-5.92*
CHCs
-2.44*
2.53*
Doctors
Dentist
-3.52*
Nurses
R2
99.4
96.8
98.3
97.4
92.1
Note: The figures in the table are presented t-coefficients and; * (**): Indicates
statistically significance at 1(5) % level.
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Table 3: Unit Root Test
==========================================================================
Level
First
Data
Difference
Inferences
Conclusion
==========================================================================
GDP
-1.13
-3.61*
I [1]
Stationary
CBR
0.51
-5.64*
I [1]
Stationary
CDR
-1.42
-6.59*
I [1]
Stationary
IMR
-1.61
-5.46*
I [1]
Stationary
LE
-0.51
-10.49*
I [1]
Stationary
CPR
-1.81
-4.04*
I [1]
Stationary
GSH
-0.46
-3.35*
I [1]
Stationary
==========================================================================
Note: GDP: Gross Domestic Product; CBR: Crude Birth Rate; CDR: Crude Death Rate;
IMR: Infant Mortality Rate; LE: Life Expectancy at Birth; CPR: Couple
Protection Rate; GSH: Government Spending on Health, and *: Indicates
statistically significant at 1%.
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Table 4: Results of Cointegration Test (Without Health Spending)
==========================================================================
Null
Maximum
Hypothesis
Eigen Value
5 % Critical Value
Trace
5 % Critical Value
Statistics
==========================================================================
H0: r = 0
64.9*
40.07
155.97*
95.75
H0: r  1
41.2*
33.88
91.07*
69.82
H0: r  2
27.7*
27.58
49.89*
47.86
H0: r  3
17.8
21.13
22.65*
29.79
H0: r  4
3.11
14.26
4.870
15.49
H0: r  5
1.76
3.841
1.761
3.841
===========================================================================
Note: r: Denotes the number of cointegrating vectors: and *: Indicates statistically
significant at 5%.
Table 5: Granger Causality Test (without Health Spending)
DV
GDP
CBR
CDR
IMR
LE
CPR
GDP
CBR
CDR
IMR
LE
CPR
Note:
: Uni-directional causality;
: No causality; other notations are
defined earlier.
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Table 6: Results of Cointegration Test (With Health Spending)
==========================================================================
Null
Maximum
Hypothesis
Eigen Value
5 % Critical Value
Trace
5 % Critical Value
Statistics
==========================================================================
H0: r = 0
108.6*
46.23
108.6*
125.62
H0: r  1
54.28*
40.08
54.28*
95.75
H0: r  2
36.51*
33.88
36.51
69.82
H0: r  3
26.91
27.58
26.91
47.86
H0: r  4
15.88
21.13
15.88
29.79
H0: r  5
4.055
14.26
4.060
15.49
H0: r  6
1.64
3.841
1.614
3.841
===========================================================================
Note: r: Denotes number of cointegrating vectors; *: Indicates statistically significant at
5%.
Table 7: Granger Causality Test (with Health Spending)
DV
GDP
CBR
CDR
IMR
LE
CPR
GSH
GDP
CBR
CDR
IMR
LE
CPR
GSH
Note: DV: Dependent variable;
: Uni-directional causality;
: No causality;
other notations are defined earlier.
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Table 8: Rank Correlation between Government Spending on Health, Health Status
and Economic Growth
===============================================================
1980
GSH
IMR
GSH
1.00
IMR
0.68*
1.00
SDP
0.81*
0.61*
GDP
1.00
===============================================================
2006
GSH
IMR
GSH
1.00
IMR
0.60*
1.00
SDP
0.68*
0.65*
GDP
1.00
===============================================================
Note: DV: Dependent variable; GHS: Government Spending on Health; IMR: Infant
Mortality Rate; SDP: Per Capita Net State Domestic Product; √: Causality exists;
and X: Causality Does Not Exist; *: Indicates statistically significant at 1%.
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Table 9: Granger Causality Test (At the State Level Analysis)
GHS and IMR
DV
SDP and IMR
SDP and GHS
IMR
GHS
SDP
IMR
SDP
GHS
Andhra Pradesh
X
X
X
√
X
√
Assam
X
X
X
X
√
√
Bihar
√
X
√
X
√
√
Orissa
X
X
√
X
√
√
Maharashtra
X
X
X
√
√
√
Madhya Pradesh
√
X
√
√
√
X
Punjab
X
X
√
√
√
√
Rajasthan
√
X
√
X
√
√
Haryana
√
X
√
X
√
√
Gujarat
X
X
√
X
√
√
Kerala
X
X
X
X
√
√
Karnataka
√
X
√
√
√
√
Tamil Nadu
X
X
X
X
√
√
Uttar Pradesh
√
X
√
√
√
X
West Bengal
√
X
√
√
√
√
Note: DV: Dependent variable; GHS: Government Spending on Health; IMR: Infant
Mortality Rate; SDP: Per Capita Net State Domestic Product; √: Causality exists;
and X: Causality Does Not Exist.
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