Relationship Between Health and Economic Growth in India

Abstract

This study employs the Granger causality test to investigate the relationship between health and economic growth in India. The annual data of major 15 states on infant mortality rate (IMR) and gross state domestic product (GSDP) from 1985 to 2015 is studied and analysed. The main finding of the study is that there is bilateral causality between GSDP and IMR. However, while testing the null hypothesis for each state, study finds the evidence of unilateral causality also, which is either running from IMR to GSDP and/or GSDP to IMR. With existence of such relationship, it is recommend that government should focus on the healthcare sector to achieve a higher rate of economic growth.

Keywords

Health economic growth Granger causality gross state domestic product

Introduction

The relationship between health and economic growth has always been at the centre of discussion since the beginning (Meer, Miller, & Rosen, 2003). Most of the researchers have a consensus view that the socio-economic standard is associated with better health standard (Preston, 1975). Over the last century, countries have witnessed an impressive improvement in economic growth and health standard (Lindert, 2004). Can health explain the economic growth of a country? This question assumes importance in the current socio-economic milieu. In this article, we contribute to the understanding of health wealth causality across Indian states. It is the income inequality which supports the low level of health standard (Pickett & Wilkinson, 2015). Whether healthier is wealthier or wealthier is healthier is the question that is tackled here.

As pointed out by Cole and Neumayer (2006), poor health has a negative impact on productivity, and health is a key factor in explaining the prevalence of underdevelopment in many countries around the world. Therefore, the question whether or not health status can stimulate economic growth has become a vital empirical issue. In fact, health can affect economic growth through its impact on human capital accumulation (Bloom & Canning, 2000; Jack & Lewis, 2009) given that healthier people are more efficient compared to those who are ill. Healthier individuals may be able to work more than those who are ill enabling them to accumulate more wealth (Adda, Chandola, & Marmot, 2003; Wu, 2003).

It is important to understand the links between health and wealth in Indian states so as to provide inputs to policymakers to help them formulate effective policies. The other reason is to contribute to the debate on health–wealth links, as higher GDP leads to better health standards through various channels such as nutrition, education, sanitation and housing (Caldwell, 1986; Musgrove, 1996). Previous studies show that causality between health and wealth should focus on wealth. In contrast, health–wealth non-causality shows that wealth may not be the single-most important factor in increasing the well-being, perhaps other development economist focusing on the development of the state of being of people have also taken up the issue (Sen, 1987). The earlier findings on health–wealth linkages suggest that along with the adoption of policies aiming to increase the health standards, other growth-enhancing programmes and policies should be introduced to have a larger impact on health standard as well as overall human development.

Objectives and Data

The objective of this article is to test the Granger causality (GC) between health and wealth by using vector autoregression (VAR) model. With the given importance of understanding the health and wealth relationship for developing countries, we are analysing the health–wealth relation in Indian states. We use gross state domestic product (GSDP) as an indicator of wealth and IMR as an indicator of health. We use IMR as a proxy for health status. The IMR is one of the most sensitive healthcare indicators since it represents the utilisation of healthcare services in a country. Before completing the first year of life, children may get affected by various infectious diseases, most of which are preventable if diagnosed on time. It has been accepted that child’s health status depends on mother’s health. Thus, IMR is also a reflection of mother’s health. Low IMR means woman enjoys a sound health standard. This model is applied using time series data on GSDP and infant mortality rate (IMR) of Indian states, observed from 1985 to 2015.

Contextualising Health of a Society Using Infant Mortality Rate

A number of studies have shown that health somehow matters for growth and it is supported by the basic economic intuition that a person with higher life expectancy will save more (e.g., Bloom et al., 2004; Boucekkine, de la Croix, & Peeters, 2007; Kinugasa & Mason, 2007; Zhang, Zhang, & Lee, 2003). The savings thus help in capital formation, thereby adding to the GDP (Zhang et al., 2003). The low level of child mortality creates an environment of low level of fertility, which limits the growth of population and thus helps in the per capita GDP growth (Kalemli-Ozcan, 2003). Infant mortality shows an adverse effect on the human capital formation, on the quality of future labour force, and ultimately on future levels of GDP (R. J. Barro, 1997). Scott and Jennifer (2002), in their study, find that the health capital has a significant impact on economic growth.

A study conducted by Asafu-Adjaye (2004) estimated the causal linkage between various factors on health status (average life expectancy and IMR) for developing and developed countries. The study found that in low-income countries, health status depends on real per capita income and this effect differs across developing and developed countries. Another study by Erdil and Yetkiner (2009a) examines the links between real per capita GDP and real per capita health expenditure in low and high-income countries. They found bidirectional and unidirectional causality between health expenditure and income. The unidirectional causality is found in income-to-health expenditure for low-income countries and health expenditure-to-income for high-income countries. Meer et al. (2003) in their study find that the direction of causality between health and wealth running from wealth to health may not be as strong as it first appears. In the data, wealth exerts a direct and statistically significant effect on health but it is very small in magnitude.

Moreover, people who are enjoying good health have a strong willpower to develop their productivity because they expect to enjoy the benefit over longer period (Bloom & Canning, 2000). Hence, transformation of health standard of the population may affect economic growth positively through its impact on human capital accumulation. In emerging economies, there are numerous micro studies in biological and social sciences showing positive effects of better health standard on productivity (e.g., Bhargava, 1997; Spurr, 1983; Strauss & Thomas, 1998). Apart from that, healthy population may accumulate more physical capital such as savings more quickly as compared to sick population because better health status will lower infant and child mortality which will result in an increase in size of working-age population. When this happens, higher savings lead to higher economic growth through higher investment (Romer, 1986; Solow, 1956). In this way, health plays a determining role in the process of economic growth through its impact on physical capital accumulation.

IMR as a Proxy for Population Health

The review of literature reveals interesting yet conflicting views about the efficacy of the IMR as a true measure of “population health”. However, most experts converge at agreement on using it as a proxy of the health of the society. We produce below some of the views on IMR as an indicator of population health:

The IMR, defined as the number of deaths in children under 1 year of age per 1,000 live births in the same year, has in the past been regarded as a highly sensitive (proxy) measure of population health (Blaxter, 1981). This reflects the apparent association between the causes of infant mortality and other factors that are likely to influence the health status of whole populations such as their economic development, general living conditions, social well-being, rates of illness and the quality of the environment.

A general measure of population health is useful for comparing the health status of a population over time or between populations at a single point in time. It permits comparisons of health systems and programmes and may highlight populations in need of particular attention from health services (Reidpath, 2003).

Some authors have gone to the extent of terming IMR as a foolproof tool of measuring health status of the population.

The infant mortality rate (IMR) is often regarded as a barometer for overall welfare of a community or country. As such, it has been used by researchers as an outcome to be explained or as an explanatory variable to capture the socioeconomic development of a country. (Aleshina & Redmond, 2005)

A number of findings also serve as policy recommendations. As an example of policy dependence on a measured association between higher IMRs and lower levels of economic development (Ozcan, 2002; Preston, 2007; Pritchett & Summers, 1996), at least two of the eight Millennium Development Goals directly refer to this metric (United Nations, 2013).

There has been enough debate comparing IMR and disability-adjusted life expectancy (DALE) and suggesting DALE as a better measure. The fact that the IMR and the DALE are so highly correlated merely goes to reinforce the intuition that the causes of infant mortality are strongly related to those structural factors such as economic development, general living conditions, social well-being and the quality of the environment, that affect the health of entire populations. This also goes some way to answer why it is that a measure of pure mortality in infants could be so highly correlated with a measure of mixed mortality and morbidity over an entire population that includes dimensions of disability and severity.

Another group of authors argues,

because IMR is sensitive to structural changes as well as disease epidemics, it becomes an important indicator of “here and now” effects. Rapid changes in the determinants of population health captured by changes in IMR may be slow to be reflected in DALE, which is influenced by factors such as long-term survival and morbidity—mirrored in features like birth cohort effects. (Murray, Salomon, & Mathers, 2000)

On the other hand, economic growth can also improve the health standards of the population through purchase of healthcare but this relationship is in concave form because health is deemed as a capital thus subject to the assumption of diminishing marginal return (Grossman, 1972). From the micro-economic viewpoint, the demand for medical care is low among the poor. As a result, the marginal rate of return for poor people to invest in health is high because low-income individual tends to be unhealthier compared to the rich people, thus a small increment of income will indirectly improve the health standard due to the increase in demand for healthcare. However, once individual reaches a healthy and wealthy state, an additional income will not make this individual healthier but stagnant. Preston (1975), in his study using macroeconomic data set, found that among the less-developed countries, increase in per capita income are positively correlated with increases in health standard measured by life expectancy. However, this relationship is weak or it even disappears when the countries approached a very high level of development. Therefore, people in less-developed countries are usually less healthy compared to the rich countries, and the relationship between health and economic growth varies with the level of development.

Methodology

To analyse the effect of health on economic growth, we use Granger causality test. Granger causality test is used to explain the two-way relationship between the two variables (GSDP and health). It is a statistical test used to test a hypothesis for determining whether a time series is affected by the other. Granger causality test was first proposed by Clive Granger in 1969. The policymakers are often unsure whether health status affected economic growth or economic growth affected health status. Thus, to check the causal relationship between economic growth and health status in India, we first specified the following two-variable VAR model:

U = (G S D P, I M R)

(1)

We then examined the result of Granger causality test to determine whether GSDP causes IMR and/or IMR causes GSDP in selected states. To investigate the Granger causal relationship, the VAR model formulated by Sims in 1980 is used with the vector U explained in Equation (1). VAR model treats each variable as a potentially endogenous variable in the system and relates each variable to its past values and to the past values of other variables that are studied in the model. We use Granger causality test in this study over other alternative methods because of its favourable response to large and small sample.

The causality test involves the testing of the null hypothesis (H₀) that economic growth (GSDP) causes IMR (health status) and vice versa. GSDP measures economic growth and IMR measures the health status in selected states.

Demetriou and Tzitziris (2017) in their study found that cross-country data demonstrate wide variation in IMRs as GDP per capita (GDPpc) varies in its range. They show that IMRs follow a convex descent trend, where there is a range of high IMRs at low GDPpc levels and there is a range of high GDPpc levels with low IMRs. They also stated that there is a clear link between IMR and GDPpc which is provided by assuming that GDPpc is subject to increasing returns.

We have following two equations:

X = C 1 \times Y t - 1 + C 2 \times X t - 1 + Ô ø Ω t

(2)

Y = C 3 \times Y t - 1 + C 4 \times X t - 1 + Ô ø Ω t

(3)

Here, X and Y are the dependent variables, C is the coefficient of the respective variable, t–1 is the lag period and µt is the random distribution or constant term.

Thus, our model will be in the form of:

G S D P = C 1 \times G S D P t - 1 + C 2 \times I M R t - 1 + Ô ø Ω t i

(4)

I M R = C 3 \times G S D P t - 1 + C 4 \times I M R t - 1 + Ô ø Ω t i

(5)

Analysis

As the first step in analysing the causality relationship between health and growth, the lag lengths are chosen for each state. Table 1 presents the lags value for each state. Consequently, we choose seven lags for Andhra Pradesh, Assam, Bihar, Gujarat, Haryana, Madhya Pradesh, Maharashtra, Orissa, Punjab, Rajasthan, Tamil Nadu and Uttar Pradesh. For Karnataka, Kerala and West Bengal, lag values are 1, 2 and 2, respectively.

Table 1.

Number of Lags Taken for GSDP and IMR

States	LAG1	LAG2	LAG3	LAG4	LAG5	LAG6	LAG7
AP	–7.934	–7.921	–7.671	–7.502	–7.846	–8.201	–10.904**
ASM	–10.362	–10.304	–10.409	–10.06	–10.491	–10.19	–11.545**
BR	–6.62	–6.347	–6.485	–6.939	–6.966	–7.379	–8.123**
GUJ	–6.786	–6.803	–6.755	–6.834	–6.843	–6.849	–7.718**
HAR	–8.802	–9.169	–9.025	–9.423	–9.186	–9.573	–10.581**
KAR	–6.609**	–6.387	–6.25	–6.115	–6.236	–6.197	–6.367
KER	–6.83	–7.308**	–7.129	–7.418	–7.166	–7.21	–7.522
MP	–8.362	–8.347	–8.165	–8.092	–8.066	–8.289	–9.529**
MAH	–6.745	–6.518	–6.207	–6.33	–6.049	–6.143	–7.659**
ORS	–7.093	–6.794	–6.862	–6.586	–6.68	–6.435	–7.418**
PUJ	–6.938	–6.804	–6.563	–6.308	–6.058	–6.386	–7.698**
RAJ	–6.573	–6.558	–6.548	–6.222	–6.387	–6.301	–7.77**
TN	–6.76	–6.674	–6.511	–6.148	–6.161	–6.532	–7.464**
UP	–8.513	–8.179	–7.97	–7.902	–8.12	–8.393	–8.849**
WB	–4.27	–4.399**	–4.155	–3.895	–3.621	–3.937	–3.84

Source: The authors.

Notes: ** Lags value. Based on Annexure A.

After choosing the lag lengths, Equations (2) and (3) are estimated for each state to test the nature and extent of relationship between GSDP and IMR. Before applying GC test to investigate the causality between GSDP and IMR, we have used VAR model in our analysis because VAR model explains the joint generation process of a number of variables over time, so we used VAR for investigating relationships between the selected variables. GC is one type of relationship between time series (Granger, 1969). The basic idea of GC can be stated as if the prediction of one time series is improved by incorporating the knowledge of a second time series, then the latter is said to have a causal influence on the first (Bose, Hravnak, & Sereika, 2017). The literature generally does not provide diversified methods for Granger (1969) causality tests in panel data models. It is possible to classify mainly two types of approaches. The first one is suggested by Holtz-Eakin et al. (1988), which considers estimation and testing VAR coefficients in panel data letting the auto regregressive coefficients and regression coefficients slopes as variable. A more or less similar method is applied by Hsiao (2014), Holtz-Eakin (1988), Glendening et al. (1996), Nair-Reichart and Weinhold (2001) and Choe (2003). The second approach proposed by Hurlin and Venet (2001), Hurlin (2004a), Hurlin (2004b), Hansen and Rand (2004) and Beyzatlar, Karacal, and Yetkiner (2012) treats the auto regregressive coefficients and regression coefficients slopes as constant (Erdila & Yetkiner, 2009a). Granger (1969) causality is defined as follows: The variable Ai,t is causing Bi,t if we are better able to predict Bi,t by using all available information, compared to the use of information without Ai,t, for each individual i є {1, N}. For matter of tractability, a number of researchers have considered only linear ones and for this reason, we have also used a time-stationary VAR representation used for a panel data set before applying GC test (Beyzatlar et al., 2012).

We have applied the VAR model to estimate the extent of the effect of health on growth and growth on health. Table 2 shows the estimation of VAR Equations (4) and (5). The coefficient values presented in the table indicates whether the relationship between GSDP and IMR is positive or negative.

Table 2.

Estimation Result of Vector Auto Regression (VAR) Equation

States	GSDP to IMR (Coefficient)	IMR to GSDP (Coefficient)	States	GSDP to IMR (Coefficient)	IMR to GSDP (Coefficient)
AP	0.9710882	0.653557	MAH	–0.1846392	–0.0574946
ASM	0.0429675	0.2204526	ORS	–0.5884208	–0.1806088
BR	–1.084082	–0.4723278	PUJ	–0.0568384	0.9026465
GUJ	0.3569838	–0.2059286	RAJ	–0.9234019	0.862589
HAR	0.1980273	1.086372	TN	–0.6357367	0.0619062
KAR	–0.1792592	–0.1781223	UP	0.0187357	0.1717303
KER	–0.0239045	2.511028	WB	–0.0029139	–2.470296
MP	0.6280941	–2.627318

Source: The authors.

Note: Based on Annexure A.

The next step is to detect the direction of the relationship between GSDP and IMR in the selected states. Towards this end, we estimate our Equation (4) and (5) for GC.

The H₀ is tested for each state and the detailed F-statistics are shown in the Tables 3 and 4.

Table 3.

Granger Causality Test Result for Causality Between Health and Growth

States	Equation	F-Value	P > F	H₀	Direction of Cause
AP	GSDP to IMR	14.243	0.0050	R	GSDP to IMR
	IMR to GSDP	2.5117	0.1639	A
ASM	GSDP to IMR	10.958	0.0090	R	GSDP to IMR
	IMR to GSDP	2.3063	0.1873	A
KER	GSDP to IMR	5.2888	0.0143	R	GSDP to IMR
	IMR to GSDP	0.7615	0.4800	A
UP	GSDP to IMR	6.2961	0.0298	R	GSDP to IMR
	IMR to GSDP	2.0587	0.2220	A
WB	GSDP to IMR	3.6895	0.0433	R	GSDP to IMR
	IMR to GSDP	2.0725	0.1521	A
MP	GSDP to IMR	3.3352	0.1017	A	IMR to GSDP
	IMR to GSDP	48.408	0.0003	R
MAH	GSDP to IMR	2.8889	0.1304	A	IMR to GSDP
	IMR to GSDP	11.506	0.0081	R
ORS	GSDP to IMR	2.1132	0.2136	A	IMR to GSDP
	IMR to GSDP	7.6378	0.0199	R
PUJ	GSDP to IMR	1.9687	0.2367	A	IMR to GSDP
	IMR to GSDP	5.7938	0.0354	R

Source: The authors.

Note: Based on Annexure A.

Table 4.

Causality Result Between Health and GSDP (Bidirectional Causal Effect)

States	Equation	F-Value	P > F	H₀	Direction of Cause
BR	GSDP to IMR	23.264	0.0016	R	Bidirectional
	IMR to GSDP	7.7571	0.0192	R
GUJ	GSDP to IMR	4.5198	0.0580	R	Bidirectional
	IMR to GSDP	8.5923	0.0154	R
HAR	GSDP to IMR	8.795	0.0146	R	Bidirectional
	IMR to GSDP	21.767	0.0018	R
KAR	GSDP to IMR	4.8025	0.0388	R	Bidirectional
	IMR to GSDP	4.1541	0.0532	R
RAJ	GSDP to IMR	12.159	0.0071	R	Bidirectional
	IMR to GSDP	7.3118	0.0218	R
TN	GSDP to IMR	8.4785	0.0159	R	Bidirectional
	IMR to GSDP	3.5531	0.0909	R

Source: The authors.

Note: Based on Annexure A.

Our H₀ is: Lagged GSDP does not cause IMR, that is, GSDP (L) ≠ IMR (L)

And/or the alternative hypothesis (H₁) is: Lagged GSDP causes IMR (Health), that is, GSDP (L) = IMR (L)

Discussion

According to Tables 3 and 4, for 6 out of 15 selected states, bidirectional causality is observed which means that in around 40 per cent of states in our data set, bidirectional causality—both from GSDP to IMR (health) and IMR (health) to GSDP—is relevant. The states such as Bihar, Gujarat, Haryana, Karnataka, Rajasthan and Tamil Nadu show bidirectional causality between GSDP and IMR. We have 9 exceptions of unilateral causality for the selected 15 states. Causality relation from IMR to GSDP is detected for Orissa, Maharashtra, Madhya Pradesh and Punjab, and causality from GSDP to IMR is seen for Andhra Pradesh, Assam, Kerala, Uttar Pradesh and West Bengal. For one-way causality, causality from GSDP to IMR is dominant for five states, that is to say, an increase in economic growth rate causes a more probable increase in health standard, that is, decreases IMR.

Though we have more states with unilateral causality, the direction of causality is not similar for all the nine states. Hence, we argue that a higher share of states show bidirectional causality may be a signal of the structural difference between the selected states. Since the Indian economy is a developing one, the states also trace a similar path to development. High-income countries or developed countries are more human-capital dependent than low income or developing countries (Erdil & Yetkiner, 2009a). In this respect, it is natural to find that income and/or GSDP (GC) causes health and/or child health and vice versa. Second, public expenditure is significantly lower in developing countries relative to developed countries and thus, in low-income countries, a negative externality might exist that can lighten the impact of income on health. Third, India is a developing country and have less-developed public infrastructure which is also a source of externality. The lack of public expenditure may increase the significance of income in explaining health due to the fact that under-developed public infrastructure increases the risk of lower life expectancy, higher mortality rates and so on, and thus lower saving investment and so on. Hence, the data is showing a causality running from GSDP to IMR or IMR to GSDP.

We divide the selected states into two groups according to their HDI rank. The first group consists of states with HDI rank above than the national HDI rank and the second group consists of all those states whose HDI rank is below the national rank. The first group consists of states such as Gujarat, Haryana, Karnataka, Kerala, Maharashtra, Punjab and Tamil Nadu. The second group includes Andhra Pradesh, Assam, Bihar, Madhya Pradesh, Orissa, Rajasthan, Uttar Pradesh and West Bengal. In the context of state groups, the share of bidirectional causality is observed at 57 per cent and 25 per cent for higher HDI states and lower HDI states. Bidirectional causality proves the importance of health in economic growth or of economic growth in health. Improved economic growth helps in advancing the healthcare facilities which in turn helps in human capital formation by reducing mortality and other health risks. We have three exceptions of bidirectional causality for higher HDI states. In Kerala, we see causality from GSDP to IMR, and in Maharashtra or Punjab, we see causality from IMR to GSDP even though these states are listed as those with higher HDI. Among the lower HDI states, higher causality obtained from GSDP to IMR means that to achieve good health standards, we have to focus on the economic growth. Lower HDI states, such as Uttar Pradesh, West Bengal, Assam, Bihar, Madhya Pradesh, Rajasthan, Andhra Pradesh and Orissa, show one-way causality run from GSDP to IMR and/or IMR to GSDP. In the lower HDI states, 50 per cent show GSDP to IMR causality. Causality relation from IMR to GSDP is detected in Madhya Pradesh, Maharashtra, Orissa and Punjab. Our model shows that majority of states display either bilateral causality or a causality from economic growth to health (GSDP to IMR) which means that more often than not, an increase in the growth rate leads to an increase in the health standards.

Conclusion

Our model shows that economic growth has a significant effect on health. The results of testing our H₀ show the bilateral causality for GSDP and IMR. However, this bilateral causality is not homogenous, which is evident from the tests of H₀ for each state. The tests for individual states show that the major type of causality is bidirectional. One-way causality is running either from IMR to GSDP and/or GSDP to IMR. This article is an addition to the existing literature to show the direction of the relationship between economic growth and health standard at the state level, that is, whether there is unidirectional causality or bidirectional causality between them. The next step would be a repeat test to find the factors responsible for the directional effect, that is, whether GSDP causes health or health causes GSDP and if there is a circular flow from health to growth or growth to health. The study recommends government to focus more on the healthcare delivery in order to achieve higher rates of economic growth.

Footnotes

Acknowledgements

The authors are grateful to the anonymous reviewer for his extremely useful comments that enriched the analysis of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Appendix

Table A3.

Lagged GSDP and IMR

OBS	PUJ		RAJ		TN		UP		WB
OBS	lnGSDP	lnIMR	lnGSDP	lnIMR	lnGSDP	lnIMR	lnGSDP	lnIMR	lnGSDP	lnIMR
1987	13.03	4.13	12.94	4.62	13.61	4.33	14.05	4.84	13.56	4.26
1988	13.08	4.13	13.24	4.63	13.69	4.30	14.17	4.82	13.60	4.23
1989	13.16	4.16	13.23	4.56	13.75	4.22	14.20	4.77	13.64	4.34
1990	13.17	4.11	13.38	4.43	13.83	4.08	14.26	4.60	13.69	4.14
1991	13.22	3.97	13.31	4.37	13.85	4.04	14.27	4.57	13.76	4.26
1992	13.26	4.03	13.44	4.50	13.91	4.06	14.29	4.58	13.80	4.17
1993	13.31	4.01	13.38	4.41	13.99	4.03	14.39	4.54	13.87	4.06
1994	13.34	3.97	13.53	4.43	14.11	4.08	14.45	4.48	13.93	4.13
1995	13.38	3.97	13.57	4.45	14.14	3.99	14.48	4.45	14.00	4.01
1996	13.45	3.93	13.68	4.44	14.19	3.97	14.58	4.44	14.07	4.01
1997	13.48	3.93	13.79	4.44	14.27	3.97	14.58	4.44	14.15	4.01
1998	13.54	3.93	13.83	4.44	14.31	3.97	14.61	4.44	14.21	3.95
1999	13.59	3.97	13.85	4.39	14.37	3.95	14.66	4.43	14.28	3.93
2000	13.63	3.95	13.83	4.37	14.43	3.93	14.69	4.42	14.32	1.61
2001	13.65	3.95	13.93	4.38	14.42	3.89	14.71	4.42	14.39	3.83
2002	13.68	3.93	13.83	4.36	14.43	3.78	14.75	4.38	14.42	3.89
2003	13.74	3.89	14.08	4.32	14.49	3.76	14.81	4.33	14.48	3.64
2004	13.78	3.81	14.06	4.20	14.60	3.71	14.87	4.28	14.55	3.69
2005	13.84	3.78	14.13	4.22	14.73	3.61	14.93	4.29	14.61	3.61
2006	13.94	3.78	14.24	4.20	14.87	3.61	15.02	4.26	14.69	3.64
2007	14.02	3.76	14.29	4.17	14.93	3.56	15.10	4.23	14.76	3.50
2008	14.08	3.93	14.37	4.14	14.98	3.43	15.17	4.20	14.81	3.56
2009	14.14	3.64	14.44	4.08	15.09	3.33	15.25	4.14	14.89	3.47
2010	14.21	3.53	14.57	4.01	15.21	3.18	15.32	4.11	14.94	3.43
2011	14.27	3.40	14.65	3.95	15.28	3.09	15.38	4.04	14.99	3.47
2012	14.31	3.40	14.71	3.95	15.32	3.09	15.44	4.04	15.06	3.47
2013	14.36	3.26	14.76	3.85	15.39	3.04	15.49	3.91	15.13	3.43

Source: Central Statistical Organization, Sample Registration System, Government of India.

References

Adda

, Chandola

, & Marmot

(2003). Socio-economic status and health: Causality and pathways. Journal of Econometrics, 112(1), 57–57.

Aleshina

, & Redmond

(2005). How high is infant and child mortality in central and Eastern Europe and the cis? Population Studies, 59(1), 39–54.

Amiri

, & Gerdtham

U. G.

(2013). Impact of maternal and child health on economic growth: New evidence based on Granger causality and DEA Analysis (Final Version). Retrieved from https://www.who.int/pmnch/topics/part_publications/201303_Econ_benefits_econometric_study.pdf

Asafu-Adjaye

(2004). Income inequality and health: A multi-country analysis. International Journal of Social Economics, 31, 195–207.

Barro

R. J.

(1997). Determinants of economic growth: A cross-country empirical study. Cambridge, MA: MIT Press.

Beyzatlar

M. A.

, Karacal

, & Yetkiner

I. H.

(2012). The Granger-causality between transportation and GDP: A panel data approach (Working papers in Economics No. 12/03). Izmir: Department of Economics, Izmir University of Economics.

Bhargava

, (1997), Nutritional status and the allocation of time in Rwandese households. Journal of Econometrics, 77, 277–295.

Blaxter

(1981). The health of the children. A review of research on the place of health in cycles of disadvantage. London, UK: Heinemann Educational Books.

Bloom

D. E.

, & Canning

(2000). The health and wealth of nations. Science, 287(5456), 1207–1209.

10.

Bloom

D. E.

, Canning

, & Sevilla

(2004). The effect of health on economic growth: a production function approach. World development, 32(1), 1–13.

11.

Bose

, Hravnak

, & Sereika

S. M.

(2017). Vector autoregressive (VAR) models and Granger causality in time series analysis in nursing research: Dynamic changes among vital signs prior to cardiorespiratory instability events as an example. Nursing Research, 66(1), 12–19. doi: 10.1097/NNR.0000000000000193

12.

Boucekkine

, de la Croix

, & Peeters

(2007). Early literacy achievements, population density, and the transition to modern growth. Journal of the European Economic Association, 5(1), 183–226.

13.

Caldwell

J. C.

(1986). Routes to low mortality in poor countries. Population and Development Review, 12, 171–220.

14.

Choe

J. I.

(2003). Do foreign direct investment and gross domestic investment promote economic growth?. Review of Development Economics, 7(1), 44–57.

15.

Cole

M. A.

, & Neumayer

(2006). The impact of poor health on total factor productivity. The Journal of Development Studies, 42(6), 918–938.

16.

Demetriou

, & Tzitziris

(2017, July 5–7). Proceedings of the World Congress on Engineering 2017 (WCE 2017, Vol. II). London. Retrieved from http://www.iaeng.org/publication/WCE2017/

17.

Erdil

, & Yetkiner

I. H.

(2009a). A panel data approach for income-health causality. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.462.1318&rep=rep1&type=pdf

18.

Glendening

E. D.

, Badenhoop

J. K.

, Reed

A. D.

, Carpenter

J. E.

, & Weinhold

(1996). NBO 3.1. Madison: Theoretical Chemistry Institute, University of Wisconsin.

19.

Granger

(1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424–438. doi: 10.2307/1912791

20.

Grossman

(1972). On the concept of health capital and the demand for health. Journal of Political Economy, 80(2), 223–255.

21.

Hansen

, & Rand

(2004). On the causal links between FDI and growth in developing countries (Miméo). Copenhagen: Development Economics Research Group (DERG), Institute of Economics, University of Copenhagen.

22.

Holtz-Eakin

(1988). Testing for individual effects in autoregressive models. Journal of Econometrics, 39(3), 297–307.

23.

Hsiao

(2014). Analysis of panel data (No. 54). Cambridge, UK: Cambridge university press.

24.

Hurlin

(2004a). Testing Granger causality in heterogeneous panel data models with fixed coefficients (Miméo). Orléans: University Orléans.

25.

Hurlin

(2004b). A note on causality tests in panel data models with random coefficients (Miméo). Orléans: University Orléans.

26.

Hurlin

, & Venet

(2001). Granger causality tests in panel data models with fixed coefficients (Miméo). Paris: University Paris IX.

27.

Jack

, & Lewis

(2009). Health investments and economic growth: Macroeconomic evidence and microeconomic foundations. Washington, DC: The World Bank.

28.

Kalemli-Ozcan

(2003). A stochastic model of mortality, fertility, and human capital investment. Journal of Development Economics, 70(1), 103–118.

29.

Kinugasa

, & Mason

(2007). Why countries become wealthy: The effects of adult longevity on saving. World Development, 35(1), 1–23.

30.

Lindert

P. H.

(2004). Growing public: Social spending and economic growth since the eighteenth century (Vol. 1: The story). Cambridge: Cambridge University Press.

31.

Liu

, Chen

, & Wang

(2015). Economic and socioeconomic determinants of infant mortality: A cross-country investigation. Retrieved from https://smartech.gatech.edu/handle/1853/54226

32.

Meer

, Miller

D. L.

, & Rosen

H. S.

(2003). Exploring the health–wealth nexus. Journal of Health Economics, 22(5), 713–730.

33.

Murray

C. J. L.

, Salomon

J. A.

, & Mathers

(2000). A critical examination of summary measures of population health. Bull World Health Organ, 2000(78), 981–94.

34.

Musgrove

(1996). Public and private roles in health (World Bank Discussion Paper No. 339). Washington, DC: World Bank.

35.

Nair-Reichert

, & Weinhold

(2001). Causality tests for cross-country panels: a New look at FDI and economic growth in developing countries. Oxford bulletin of economics and statistics, 63(2), 153–171.

36.

Ozcan

(2002). Does the mortality decline promote economic growth? Journal of Economic Growth, 7, 411–439.

37.

Pickett

K. E.

, & Wilkinson

R. G.

(2015). Income inequality and health: A causal review. Social Science & Medicine, 128, 316–326.

38.

Preston

S. H.

(1975). The changing relation between mortality and level of economic development. Population Studies, 29(2), 231–248

39.

Preston

S. H.

(2007). The changing relation between mortality and level of economic development. International Journal of Epidemiology, 36(3), 484–490.

40.

Pritchett

, & Summers

(1996). Wealthier is healthier. Journal of Human Resources, 31(4), 841–868.

41.

Reidpath

D. D.

, & Allotey

(2003). Infant mortality rate as an indicator of population health. Journal of Epidemiology & Community Health, 57(5), 344–346.

42.

Romer

(1986). Increasing returns and long-run growth. Journal of Political Economy, 94(5), 1002–1037.

43.

Scott

, & Jennifer

(2002). Growth and multiple forms of human capital in an augmented Solow model: A panel data investigation. Economics Letters, 74(2002), 271–276.

44.

Sen

(1987). The standard of living: The Tanner lectures. Cambridge: Cambridge University Press.

45.

Solow

(1956). A contribution to the theory of economic growth. Quarterly Journal of Economics, 70, 65–94.

46.

Spurr

G. B.

(1983). Nutritional status and physical work capacity. American Journal of Physical Anthropology, 26, 1–35.

47.

Strauss

, & Thomas

, (1998). Health, nutrition and economic development. Journal of Economic Literature, 36, 766–817.

48.

United Nations (2013). Millennium development goals and beyond 2015. Retrieved from http://www.un.org/millenniumgoals/

49.

Upadhyay

A. K.

, & Srivastava

(2017). Association between Haemophilus influenza type B (Hib) vaccination and child anthropometric outcomes in Andhra Pradesh (India): Evidence from the Young Lives Study. Journal of Public Health, 25(6), 581–589.

50.

S. I.

(2003). The relationship between consumer characteristics and attitude toward online shopping. Marketing intelligence & planning.

51.

Zhang

, Zhang

, & Lee

(2003). Rising longevity, education, savings, and growth. Journal of Development Economics, 70, 83–101.