The Direction of Causality between Health Spending and GDP: The Case of Pakistan
AbstractRelevant literature suggests that the most important determinant of health care spending is real GDP. Moreover, there is considerable evidence that health care spending rises at a faster rate than real GDP. This paper uses recently developed tests for the existence of a long run relationship to analyze the links between health care spending and GDP. We are, particularly, interested in estimating the elasticity parameter. The aim of the paper is to provide a new method of analysis to those used in recent papers on this subject. Typically in applied analysis, testing for the existence of cointegration and causality can only be carried out once the time series properties of the data have been established. For example, tests for cointegration require the variables to integrated of the same order, typically I(1), prior to estimation. By eliminating the need for unit root pre-testing, the tests applied here considerably simplify the inference procedure. They also reduce the potential for distortions in the inference due to the unknown properties of the testing sequence. Our findings include robust evidence that, for Pakistan, the income elasticity for health care spending is greater than one and that the elasticity value is stable over the estimation period.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 23379.
Date of creation: 15 Aug 2006
Date of revision: 05 Dec 2006
Publication status: Published in Pakistan Economic and Social Review 1.45(2007): pp. 125-140
Health Spending; GDP; Causality;
Find related papers by JEL classification:
- H51 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Health
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
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