Life expectancy at birth, widely used as an indicator of overall development of a country, has increased over the last ten years in most of the countries of the world. This is of particular significance for the post-colonial world since they hope to achieve socio-economic progress through investing significantly on social sectors like health, education, sanitation, environmental management and sustainability, and social safety nets. Improvements in incidence of poverty, malnutrition, adult literacy, access to safe drinking water, and sanitation have also been remarkable over the years that would have impacted positively on life expectancy.
But life expectancy which has exhibited patterns of continuous growth over time, has also demonstrated persistently high variability between countries over the past half-century. As of 2017, the gap in life expectancy between regions classified by the United Nations (UN) as more developed and less developed is as high as 11 years. Developed countries are known to have outpaced developing and under-developed countries in respect to their demographic structure as well as their economies. The main reason is due to the fact that developed countries are expected to have reached their goals by having an ideal economic structure and are intending to sophisticate their economies and global image. On the opposite side, developing countries are still in the transition of demographic and economic changes, and thus they can be compared to with developed countries so as to apply changes to their current models and hence reach their expected economic goals.
Thus, it is important to analyse the demographic and economic factors affecting life expectancy among the developed and developing countries of the world
Life expectancy at birth, is an important function that tell us how well the country is performing. Works like Kabir (2008), Shaw, Horrace, and Vogel (2005), Rodgers (1979) clearly points out the high correlation between GDP per capita & life expectancy. Adekola (2002) has shown that life expectancy increases as income per capita increases.
Apart from these factors like health expenditure as a portion of total public expenditure of a country, education or literacy rate have a huge impact on life expectancy in case of developing nations. In Kabir (2008) it has been emphasized that life expectancy in developing nations has been increasing due to significant improvement in public sectors like education, basic health care & sanitation.
In Rodgers (1979), it has been argued that increase in per capita income allows an individual to get better medical facilities and hence improve life expectancy Strulik (2015) found that the cardiovascular revolution led to a rise in life expectancy by 2 years which further helped to increase higher education enrolment by 7% per unit in US. Hazen (2012) has also identified that there is a positive correlation between life expectancy and average years of schooling.
An important argument has been put forward by Deaton concerning the relation between Life expectancy and income. He shows that income does not necessarily have a positive correlation with Life expectancy. He argues that we should look at the distribution of GDP among the populace. He says that at lower levels marginal increase in income leads to significant gains in life expectancy but after a certain threshold variance in improvement or disimprovement of life expectancy cannot be suitably explained by income. In this regard Prof. Amartya Sen gives the example of the Indian state of Kerala which has managed to achieve impressive life expectancy at a relatively low level of income.
This paper seeks to examine whether the same factors can explain fluctuation in life expectancy in both high income economies and low-income economies. We take life expectancy to be a function of the economic variables incorporate per capita GDP, per capita public and private health expenditure, urbanization, fertility rate, and medical care inputs, whereas non-economic variables incorporate nutritional status, access to safe drinking water, and dummy for geographical location of a country. We seek to check if the regressors we have taken are significant for both high income and low-income countries and if differences arise between high income and low-income countries what measures should be taken by developing countries so as to enhance the health of the populace. Thus, we shall do comparative analysis of determinants of life expectancy in developed and developing countries.
Data are taken from the OECD Health data 2016 database (http//dx.doi.org/10.1787/9789264265592-en),
Global Economic Prospects-June 2017
Global Health Observatory(GHO) data, WORLD HEALTH ORGANISATION