The Causal relationship between Infant mortality rate, Health expenditure and Economic growth in India.

Prakash Kengnal, Sharnkumar Holyachi


Background: The Infant Mortality Rate defined as the risk for a live born child to die before its first birthday is known to be one of the most sensitive and commonly used indicators of the social and economic development of a nation. Method: This paper investigates the causal relationship between infant mortality rate, economic growth and private health expenditure (% GDP) in India using the cointegration and Granger causality frameworks for the period from 1995 to 2013. We examine the presence of a long-run equilibrium relationship using the bounds testing approach to cointegration within the Unrestricted Error- Correction Model (UECM). Moreover, we examine the direction of causality between infant mortality rate, economic growth and private health expenditure (% GDP) in India using the Granger causality test within the Vector Error-Correction Model (VECM). Results:As a summary of the empirical findings, we find the Infant Mortality Rate (IMR), Per capita gross domestic product (PCGDP) and private health expenditure (% GDP) are cointegrated.  The results of Granger Causality suggest that no short-run effect was existing between all the three variables. The error-correction term implies that the variable is non-explosive and long-run equilibrium relationship is attainable. Conclusion: The study indicates that as per capita gross domestic product and private health expenditure increases, infant mortality rate decreases.


Infant mortality rate, Private Health Expenditure (% GDP), Per Capita Gross Domestic Product (PCGDP), Cointegration and Granger Causality.

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Definitions used in National Health Profile. Central Bureau of Health Intelligence (CBHI), Government of India, 2013.

Park K. Parks textbook of preventive and social medicine (23rd ed). Banarisidas Bhanot: Jabalpur India, 2015.

SRS bulletin. Sample Registration System, Registrar General of India. Government of India, 2012.

Gupta S, Verhoeven M and Tiongson, E. Public spending on health care and the poor. Unpublished manuscript. 2001

Agrawal D and Hedau V. Determinants of infant mortality rate in India. International Journal of Research in Management Science and Technology 2014;2:32-42.

Chang T, FangW and Wen LF. Energy consumption, employment, output and temporal causality: evidence from Taiwan based on cointegration and error- correction modeling techniques. Applied Economics 2001;33:1045–1056.

Stern DI. Energy growth in the USA: A multivariate approach. Energy Economics 1993;15:137–150.

Stern DI. A multivariate cointegration analysis of the role of energy in the US macroeconomy. Energy Economics 2000;22:267–283.

Pesaran M H and Shin Y. An autoregressive distributed lag modelling approach tocointegration analysis. In: Storm, S. (Ed.), Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium (pp. 1–31). Cambridge University Press: Cambridge,1999.

Pesaran MH, Shin Y and Smith R J. Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics 2001;16:289–326.

Narayan PK and Smyth R. Electricity consumption, employment and real income in Australia evidence from multivariate Granger causality tests. Energy Policy 2005;33:1109–1116.

Halicioglu F. An econometric study of CO2 emissions, energy consumption, income and foreign trade in Turkey. Energy Policy 2009;37:1156–1164.

Ghosh S. Import demand of crude oil and economic growth: evidence from India. Energy Policy 2009;37:699–702.

Odhiambo NM. Energy consumption and economic growth nexus in Tanzania: an ARDL bounds testing approach. Energy Policy 2009;37:617–622.

Hakkio CS and Rush M. Cointegration: how short is the long run? Journal of International Money and Finance 1991;10: 571-581.

World Bank. World Development Indicators (WDI) Online Database. Washington D.C., USA: The World Bank, 2015.

Granger CWJ and Newbold P. Spurious regressions in econometrics. Journal of Econometrics 1974;2:111-120.

Phillips PCB . Understanding spurious regressions in econometrics. Journal of Econometrics 1986;33:311-340.

Engle RF and Granger CWJ. Cointegration and error correction: representation, estimation and testing. Econometrica 1987;55: 251–276.

Johansen S and Juselius K. Maximum likelihood estimation and inference on cointegration with application to money demand. Oxford Bulletin of Economics and Statistics 1990;52:169–210.

Bahmani-Oskooee M and Bohl M. German monetary unification and the stability of the German M3 money demand function. Economics Letters 2000;66:203-208.

Bahmani-Oskooee M. How stable is M2 money demand function in Japan? Japan and the World Economy 2001;13: 455-461.


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