The Impact of Economic Indicators on Life Expectancy: A Panel Data Analysis
In this study, economic indicators which have an effect on life expectancy were investigated for OECD countries in the early 21st century. When the development levels of countries and their life expectancy are examined, it is seen that underdeveloped countries are significantly behind the life expec...
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Veröffentlicht in: | İstanbul iktisat dergisi (Online) 2020-01, Vol.70 (1), p.163 |
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description | In this study, economic indicators which have an effect on life expectancy were investigated for OECD countries in the early 21st century. When the development levels of countries and their life expectancy are examined, it is seen that underdeveloped countries are significantly behind the life expectancy at birth compared to developed countries. For this reason, it is of great importance to find out why the length of life is higher in economically developed countries and to investigate the economic indicators affecting this situation. When the studies in the literature are evaluated, it is seen that despite the importance of the subject, there are not enough studies examining the relationship between life expectancy and economic variables. In this study, Panel Regression Analysis and Panel Granger Causality Analysis were used for the period 20002016. According to the findings of the study, bidirectional causality was determined between life expectancy and unemployment rate, and life expectancy and gross domestic product. Also, unidirectional causality relationship was determined from health expenditures to life expectancy. In addition, regression analysis revealed that health expenditures are the most important factor affecting the life expectancy for OECD countries. This result shows that in terms of development, providing a better life to individuals and society is possible when the health expenditure of countries is increased to a higher level. |
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Also, unidirectional causality relationship was determined from health expenditures to life expectancy. In addition, regression analysis revealed that health expenditures are the most important factor affecting the life expectancy for OECD countries. This result shows that in terms of development, providing a better life to individuals and society is possible when the health expenditure of countries is increased to a higher level.</description><identifier>ISSN: 2602-4152</identifier><identifier>EISSN: 2602-3954</identifier><identifier>DOI: 10.26560/istjecon.761472</identifier><language>eng</language><publisher>Istanbul: Istanbul Universitesi/Istanbul University</publisher><subject>Causality ; Economic indicators ; Economic theory ; GDP ; Gross Domestic Product ; Health care expenditures ; Industrialized nations ; Life expectancy ; Longitudinal studies ; Regression analysis</subject><ispartof>İstanbul iktisat dergisi (Online), 2020-01, Vol.70 (1), p.163</ispartof><rights>2020. 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When the development levels of countries and their life expectancy are examined, it is seen that underdeveloped countries are significantly behind the life expectancy at birth compared to developed countries. For this reason, it is of great importance to find out why the length of life is higher in economically developed countries and to investigate the economic indicators affecting this situation. When the studies in the literature are evaluated, it is seen that despite the importance of the subject, there are not enough studies examining the relationship between life expectancy and economic variables. In this study, Panel Regression Analysis and Panel Granger Causality Analysis were used for the period 20002016. According to the findings of the study, bidirectional causality was determined between life expectancy and unemployment rate, and life expectancy and gross domestic product. 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subjects | Causality Economic indicators Economic theory GDP Gross Domestic Product Health care expenditures Industrialized nations Life expectancy Longitudinal studies Regression analysis |
title | The Impact of Economic Indicators on Life Expectancy: A Panel Data Analysis |
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