The Effect of Human Health on Sustainable Development in India: the State Level Analysis

2012Cambridge Business & Economics ConferenceISBN : 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

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@: 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:

2: Director, Narsee Monjee Institute of Management Studies (Mumbai) Shirpur Campus, Dhule 425405, India. Email:

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 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 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 IVworks 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.

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 P1 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:

………. (2)

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 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

Note: CIII: Composite Index of Input Infrastructure; CIOI: Composite Index of Output Infrastructure

Source: Authors Calculation

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 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 InputsHealth OutputsEconomic Growth

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:

………. (3)

Where, Z = {GDP, CBR, CDR, IMR, LE, CPR), Δ is the first difference operator and is the lagged stationary residuals from the cointegration equation. The test is to reject the null hypothesis of non-causality between healthand economic growthagainst 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.

………. (4)

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 1and H0: η1 = 0 against H0: η1 ≠ 0 for equation 2respectively. 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.

………. (5)

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 is stationary [I (0)] even though Xt is non-stationary [I (1)].

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:

………. (7)

Where , ….. 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

………. (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, sub-centres, 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 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.