Methodology for an Optimum Health Expenditure Allocation

2020 ◽  
pp. 215-223
Author(s):  
George Matalliotakis
2018 ◽  
Vol 6 (3) ◽  
pp. 1
Author(s):  
Kok Wooi Yap ◽  
Doris Padmini Selvaratnam

This study aims to investigate the determinants of public health expenditure in Malaysia. An Autoregressive Distributed Lag (ARDL) approach proposed by Pesaran & Shin (1999) and Pesaran et al. (2001) is applied to analyse annual time series data during the period from 1970 to 2017. The study focused on four explanatory variables, namely per capita gross domestic product (GDP), healthcare price index, population aged 65 years and above, as well as infant mortality rate. The bounds test results showed that the public health expenditure and its determinants are cointegrated. The empirical results revealed that the elasticity of government health expenditure with respect to national income is less than unity, indicating that public health expenditure in Malaysia is a necessity good and thus the Wagner’s law does not exist to explain the relationship between public health expenditure and economic growth in Malaysia. In the long run, per capita GDP, healthcare price index, population aged more than 65 years, and infant mortality rate are the important variables in explaining the behaviour of public health expenditure in Malaysia. The empirical results also prove that infant mortality rate is significant in influencing public health spending in the short run. It is noted that macroeconomic and health status factors assume an important role in determining the public health expenditure in Malaysia and thus government policies and strategies should be made by taking into account of these aspects.


2019 ◽  
Author(s):  
Joses Kirigia ◽  
Rose Nabi Deborah Karimi Muthuri

<div>A variant of human capital (or net output) analytical framework was applied to monetarily value DALYs lost from 166 diseases and injuries. The monetary value of each of the 166 diseases (or injuries) was obtained through multiplication of the net 2019 GDP per capita for Kenya by the number of DALYs lost from each specific cause. Where net GDP per capita was calculated by subtracting current health expenditure from the GDP per capita. </div><div> </div><p>The DALYs data for the 166 causes were from IHME (Global Burden of Disease Collaborative Network, 2018), GDP per capita data from the International Monetary Fund world economic outlook database (International Monetary Fund, 2019), and the current health expenditure per person data from the WHO Global Health Expenditure Database (World Health Organization, 2019b). A model consisting of fourteen equations was calculated with Excel Software developed by Microsoft (New York).</p><p> </p>


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