Disease, War, Hunger, and Deprivation: A Cross-National Investigation of the Determinants of Life Expectancy in Less-Developed and Sub-Saharan African Nations

Abstract

Researchers note a recent trend of increasing inequality in cross-national life expectancy rates, largely due to conditions in the poorest of nations. Threats to life expectancy in less-developed nations include poverty, warfare, intense hunger, and disease, particularly AIDS/ HIV. This article utilizes structural equation models for a sample of lessdeveloped nations and a subsample of Sub-Saharan African nations to test interrelationships among predictors. Findings indicate modernization to be the most robust predictor of life expectancy across less-developed nations and HIV to be the strongest determinant of life expectancy in Sub-Saharan African nations. Somewhat surprisingly, warfare and hunger do not have direct impacts on life expectancy among less-developed nations; however, important linkages among warfare, hunger, and disease are evidenced in the Sub-Saharan African sample, along with a notable positive influence of modernization on HIV rates. The findings demonstrate the significance of HW on cross-national life expectancy scores and illuminate unique dynamics in Sub-Saharan Africa.

Modeling the influence of foreign investments on food security based on panel cointegration models

“…Only in a few works, however, is panel data analysis used as an econometric tool for the full sample of developing countries [9,12,14]. In most works, the authors confine themselves to descriptive data analysis.…”

“…Among all the ones proposed, it can distinguish the average energy value of the diet, the percentage of newborns weighing less than 2500 grams at birth, the proportion of foods consumed containing starch [19], daily protein intake [6,12], the proportion of undernourished [14], etc. It can be concluded that among researchers there is no single opinion and substantive justification for answering the question which of the 30 indicators should be chosen as a measure of food security.…”

Do Military Expenditure and Conflict Affect Economic Growth in Sri Lanka? Evidence from the ARDL Bounds Test Approach

“…The Uppsala conflict database differentiates conflict into major and minor based on battle-related death. Empirical studies by Gates et al (2012), Austin and Mckinney (2012), Jeanty and Fred (2006) and Sandler (2002a &2002b) have also used battle related death in their studies to measure conflict-effect. Dunne et al (2005) highlighted that the Solow model is the most suitable among the existing theoretical models to examine the military-growth nexus.…”

Dependency, Urban Slums, and the Forgotten Plagues

“…SEM is a powerful modeling framework with a long-standing tradition in sociological research (Bollen 1989;Duncan 1966) and is also gaining prominence in comparative assessments of human health (e.g., Austin and McKinney 2012;Wickrama and Lorenz 2002). SEM is a powerful modeling framework with a long-standing tradition in sociological research (Bollen 1989;Duncan 1966) and is also gaining prominence in comparative assessments of human health (e.g., Austin and McKinney 2012;Wickrama and Lorenz 2002).…”

“…I therefore measure socio-health characteristics using a latent construct that is comprised of the following indicators: number of trained medical personnel, participation in secondary schooling, and the fertility rate. Schooling has been linked to improvement of a variety of health measures, such as life expectancy, infant mortality, and HIV rates (e.g., Austin and McKinney 2012;Brady et al 2007;Burroway 2010), due to the fact that educated people tend to have more knowledge of disease vectors and preventive behaviors, and are also more likely to seek medical attention when ill. Data were acquired from the World Bank (2013) and measured for the year 2005.…”