文章基本信息

标题：Impact of Socio-Economic Variables on Life Expectancy: An Empirical Study for 138 Countries
本地全文：下载
作者：Mahmoud MOURAD
期刊名称：Journal of Public Administration and Governance
电子版ISSN：2161-7104
出版年度：2021
卷号：11
期号：1
页码：330-346
DOI：10.5296/jpag.v11i1.18437
出版社：Macrothink Institute
摘要：This study has examined the impact of mortality rate of children under five years of age (MORRATE), physicians (PMP), health expenditure per person (HEPP), access to electricity (AELEC) and GDP per capita on life expectancy at birth (LEB) for one hundred and thirty-eight countries taken as cross-sectional data. The MORRATE ranged from 2.4 to 160.2 (per 1,000 people), thus reflecting an inequality in LEB which fluctuates between 44.8 and 82.8. The PMP varies from 0.01 to 7.74, the HEPP between 16.92 and 8264 USD, the AELEC between 4.1% to 100% and finally the GDP per capita oscillates between 326.6 and 102,863 USD. The multiple linear regression model is estimated using the OLS method and several tests for heteroscedasticity are performed. The null hypothesis of homoscedasticity is rejected and therefore the Weighted Generalized Least Squares ) WGLS) method is used to produce unbiased, efficient and consistent estimators. The results showed a negative impact of MORRATE on LEB. A single increase in the number of deceased children leads to a decrease of about 2.12 months in LEB. The HEPP has a positive impact on LEB, so if HEPP rises to 100 USD then the LEB rises by 33 days approximately. When introducing four binary variables characterizing the five continents, and taking Oceania as a reference, the life expectancy in an African country will be about 2.4 years less than the LEB reference. For the other continents, it seems that the values of LEB are very close.
关键词：multiple linear regression modeling; estimation; heteroscedasticity; tests; life expectancy; binary variables.