Life expectancy influenced by HDI and GDP

Literature review

In this section, we will in the first step do a brief review of theoretical literature before presenting in a second step the results of some empirical studies.

I & II articles assured that Life Expectancy is a valid indicator of representing Population health statue.

Jean Marie Robine, Karen Ritchie, 1991, “Health life expectancy: evaluation of global indicator of change in population health” BMJ vol.302: 457-460 This text outlines ‘Healthy life expectancy’ is a valuable index for the appreciation of changes in both the physical and the mental health states of the general population, for allocating resources, and for measuring the success of political programs. It states that future calculations should also take into account the probability of recovery and thus extend the applicability of the indicator to populations in poor health rather than focusing on the well population.

Gabriel Gulis, 2000, “Life expectancy as an indicator of environmental health” European Journal of Epidemiology vol. 16, no.2 :161-165 This article questioned that whether or not life expectancy at birth is related to the quality of life as expressed by global economic, environmental and nutritional measures. To get an answer, two models set of independent variables and multivariate analysis was performed. An attempt to estimate the role of studied variables in overall life expectancy was done, too. Access to safe drinking water per capita gross domestic product, literacy, calories available as percentage of needs and per capita public health expenditures were taken as exposure, and compared with life expectancy at birth. A linear regression model was used to estimate the role of different exposures on life expectancy at birth. In the result, the correlation coefficient for the linear model was 0.8823 (R2 =0.7784)

Harttgen and Klasen (2012) who took an initiative to analyze the micro-level distribution of HDI. HDI is a macroeconomic indicator by default and even adjusting it to inequality measures, as mention above, fails to incorporate micro level phenomenon. However, it is more knowledge and useful for policy purposes to investigate disparity in HDI among different economic and social groups including households. By calculating household level components of HDI, it was established that in some countries with low income equalities, we have witnessed high level of HDI inequality among different social groups. Similarly, the reverse result was also observed. The technique made it feasible to focus on intra-country level results and allowed a room for policy recommendations for various sections of the country

Dr. Gary Becker, an economist at the University of Chicago, also released a paper showing the gap inaccuracy of current models that show the correlation between per capita GDP and life expectancy (2005). While Becker’s paper focuses on the lack of accuracy in the model, the reason stated for such variance is not entirely in concert with Deaton’s research. The major gap, as Becker found, comes from income inequality across countries rather than differences in how GDP is spent.

Among recent papers, Bhargava et al. (2001) found that the effect of health on the growth rates of GDP per capita was positive only for low income countries, in a panel estimation of 92 countries from 1965-1990. Jamison et al. (2005) reported similar results in their estimation of a panel of 53 countries from 1965-1990. They found that the positive effects of health on GDP per capita declined as life expectancy increased, that is, the effects were larger for low income countries with lower life expectancy.

Research methodology

For this study the methodology used for analysing our data set is the regression analysis. Regression analysis is a quantitative research method which is used when the study involves modeling and analysing several variables, where the relationship includes a dependent variable and one or more independent variables. The basic form of regression models includes unknown parameters (β), independent variables (X), and the dependent variable (Y). Regression model, basically, specifies the relation of dependent variable (Y) to a function combination of independent variables (X) and unknown parameters (β). (https://research-methodology.net/research-methods/quantitative-research/regression-analysis/ accessed on: 01/12/2017)

Regression equation can be used to predict the values of ‘y’, if the value of ‘x’ is given, and both ‘y’ and ‘x’ are the two sets of measures of a sample size of ‘n’. Regression models can be simple linear models or multiple models. The formulae for the regression equations would be:

Simple linear regression:

Y= β_0+ β_1 X+ε , where ε is the error term

Multiple regression:

Y= β_0+ β_1 X_1+ β_2 X_2+⋯+β_n X_n+ε , where ε is the error term

After regression analysis is conducted using EViews, an interpretation of the values obtained will be made, in order to explain the meaning of some statistics, for example: