Health impact on Economy by Artificial Neural Network and Dynamic Ordinary Least Squares

Marziyeh Sadat Safe, Mohsen Barouni, Seyed Mojtaba Saif


 Introduction: Achievement of economic growth, as one of the most important macroeconomic variables, depends on the precise understanding of potential routes and the factors affecting on it. The aim of this study was to evaluate the health care sector’s effect on Iran Gross Domestic Product (GDP), as the status of economy.

Method: Artificial Neural Network (ANN) and Dynamic Ordinary Least Squares (DOLS) were performed according to Iran GDP as the output variable and the input variables of life expectancy at birth, under five mortality rates, public health expenditures, the number of doctors and hospital beds during 1961-2012 in Iran. Data were collected from the Statistical Center of Iran, the Central Bank of the Islamic Republic of Iran, the World Health Organization and the World Bank databases. Data management and analysis were performed using Eviewes 7, stata 11 and also Mathlab. MSE, MAE and R2 were calculated to assess and compare the models.

Results: One percent reduction in deaths of children under 5-years could improve Iran GDP as much as 1.9%. Additionally, one percent increment in the number of doctors, hospital beds or health expenditure would increase GDP by 0.37%, 0.27% and 0.29%, respectively. Mean Absolute Error (MAE) demonstrated the superiority of DOLS in the model estimation.

Conclusion: The lack of sufficient considerations and excellent models in the health care sector is the main reason for underestimating the effect of this sector on economy. This limitation leads to neglecting the resource allocation to the health care sector, as the great potential motivation of the economic growth.

Keywords: Neural Network, Health care sector, Life expectancy, Health expenditure, Econometric model

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