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

Marziyeh Sadat Safe, Mohsen Barouni, Seyed Mojtaba Saif

Abstract


 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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Abrishami H, MEHRARA M, AHRARI M, Mirghasemi S. Forecasting the GDP in Iran Based on GMDH Neural Network. 2009.

Zeranezhad M, Khodapanah M, Kiyani P, Ebrahimi S. [Comparison of performance of fuzzy autoregressive integrated moving average and fuzzy neural network to forecast economic growth in Iran]. Quarterly Journal of Applied Economics Studies in Iran. 2014;2(8):33-52.

Dehmardeh N, Esna Ashar H, Sardar Shahraki A. using the approach artificial neural network and Atu Regressive methods with growth forecast of Iran’s economic. Economic Development Research. 2013;3(9):73-8.

Zarra-Nezhad M, Hosainpour F. Review of Growth Models in Less Developed Countries. The International Journal of Applied Economics and Finance. 2011;5(1):1-17.

Arora S. Health, human productivity, and long-term economic growth. The Journal of Economic History. 2001;61(3):699-749.

Mirbagheri M, Tagiev N. Analyzing economic structure and comparing the results of the predicted economic growth based on solow, fuzzy-logic and neural-fuzzy models. Technological and Economic Development of Economy. 2011;17(1):101-15.

Beheshti M, Sojudi S. Empirical Analysis of the Relationship between Health Expenditure and GDP in Iran. Faslname‐Barresi ha ye eghtesadi. 2007;4(4):115-35.

Emadzadeh M, Sameti M, Safi Dastjerdi D. [The Effects of Healthcare Expenditure on Economic Growth of Iranian Provinces]. Journal of Health Information Management. 2012;8:918-28.

Hadian M, Shojaee S, Rajabzadeh D. The impact of health expenditure on the economic growth in IRAN (1980-2004). Journal of Health Administration. 2006;9(24):39-44.

Lotfalipour M, Falahi M, Borji M. The effects of health indices on economic growth in Iran. Journal of Health Administration. 2012;14(46):57-70.

Salmani B, Mohammadi A. Investigating effect of government health expenditure on Iran’s economic growth. Iranian Journal of Economic Research. 2009;13(39):73-93.

Kirigia JM, Muthuri RDK, Nabyonga-Orem J, Kirigia DG. Counting the cost of child mortality in the World Health Organization African region. BMC Public Health. 2015;15(1).

Samadi AH, Keshtkaran A, Kavosi Z, Vahedi S. The Effect of Fiscal Decentralization on Under-five Mortality in Iran: A Panel Data Analysis. Int J Health Policy Manag. 2013;1(4):301-6.

Rhodes A, Ferdinande P, Flaatten H, Guidet B, Metnitz PG, Moreno RP. The variability of critical care bed numbers in Europe. Intensive Care Med. 2012;38(10):1647-53.

Rezaei S, Fallah R, Karyani AK, Daroudi R, Zandiyan H, Hajizadeh M. Determinants of healthcare expenditures in Iran: evidence from a time series analysis. Medical journal of the Islamic Republic of Iran. 2016;30:313.

Hamoudi AA, Sachs JD. Economic consequences of health status: a review of the evidence: Center for International Development at Harvard University; 1999 Contract No.: Document Number|.

Tkacz G. Neural network forecasting of Canadian GDP growth. International Journal of Forecasting. 2001;17(1):57-69.

Ge L, Cui B, editors. Research on forecast of GDP based on process neural network. Natural Computation (ICNC), 2011 Seventh International Conference on; 2011. IEEE.

Eswaran C, Logeswaran R. A dual hybrid forecasting model for support of decision making in healthcare management. Advances in Engineering Software. 2012;53:23-32.

Ghorashi N, Rad A, Eslami M. The Study On Factors of Health Economics and Economic Growth in Iran. Journal of Community Health Research. 2013;2(3):208-19.

Kazemi M, Jalaee SA, Abadi E, Fard HA. Investigation the Impact of Exchange Rate Uncertainty on Economic Growth in Iran by Neural Networks. 2014.

Mladenović I, Milovančević M, Sokolov Mladenović S, Marjanović V, Petković B. Analyzing and management of health care expenditure and gross domestic product (GDP) growth rate by adaptive neuro-fuzzy technique. Computers in Human Behavior. 2016;64:524-30.

SADEGHI SK. THE LONG-RUN RELATIONSHIP EDUCATION, HEALTH, SECURITY AND SOCIAL WELFARE EXPENDITURES AND ECONOMIC GROWTH IN ORGANIZATION OF ISLAMIC COUNTRIES (DYNAMIC ORDINARY LEAST SQUARE IN PANEL DATA). 2015.

ZarraNezhad M, Mansouri S. Dynamic Simulation of Unemployment Rate in Iran. Bi-annual Journal of Economics Studies and Policies. 2014;10(1):75-106.

SETAYESH M, EBRAHIMI F, SAIF S, SARIKHANI M. FORECASTING THE TYPE OF AUDIT OPINIONS: A DATA MINING APPROACH. 2013.

Gevrey M, Dimopoulos I, Lek S. Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling, 2003; 160(3), 249-264.

PANAHI H, ALEEMRAN SA. The relationship between mortality of infant under one year and poverty, urbanization and GDP per capita in Iran. 2015.

Fattahy S, Soheili K, Reshadat S, Karimi P. The relationship between health human capital,and economic growthin the countries of OPEC. Quarterly Journal of Healthcare Management. 2012;3((3, 4)):37-51.

Manzour D, NouriInaloo A. The estimation of domestic energy demand in Iran: Dynamic Ordinary Least Squares approach. Journal of Imam Sadiq University. 2006;27:7-21.

Ugalde, H. M. R., Carmona, J.-C., Alvarado, V. M., & Reyes-Reyes, J. (). Neural network design and model reduction approach for black box nonlinear system identification with reduced number of parameters. Neurocomputing, 2013;101, 170-180


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