Determinants Of Life Expectancy Health And Social Care Essay
Mahfuz (2005 pp.185) quotes: “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 has a particular indication for the developing world since they are striving earnestly for achieving socio-economic progress”.
The relation between life expectancy and socio-economic factors can be used to evaluate possible demographic changes which can be induced by national development programmes. The results of this study will therefore be encouraging for economic development; Adelman (1963).The major area of research in this paper will focus on casual factors, that will be linked operationally to life expectancy of the individuals in both developed and developing countries. The analysis of life expectancy is important for theories of development, so this research will typically focus on determining the dependence of life expectancy on birth rate, death rate, infant mortality, health expenditure per capita and per capita gross national product (GNP).
According to Murray (1988) life expectancy and infant mortality are indicators of health status needed to access the performance of a country. He claims that fertility rate and infant mortality have a positive relation, where fertility rate is the measure of crude birth rate. This explains that if the infant mortality rate increases, it will lead to an increase in fertility rate as people will not be sure whether their child will survive and give birth to more children.
Simon (1969) believes that with the rise in average income, birth rate falls in less developed countries. He also tells us that the unconditional effect of income on fertility is negative in the underdeveloped countries but might be positive if partial effect is considered. Weintraub (1962, pp.813) also believes that “relationship between the birth rate and per capita income (is) negative.” But at the same time he suggests that there can also be a case that with increase in GDP, the family income rises to raise more children and in turn birth rate rises.
Adelman (1963) suggests that “homogeneity” exists in the response to population pressure in both developed and less developed counties. There is a negative relation found between death rates and economic condition. Also there is found to be negative partial correlation between death rate and rate of growth of real per capita income. With increase in death rates, the growth of GDP and economic conditions will decrease and eventually lead to a decrease in life expectancy.
Waldmann (1992, pp.1283) quotes “Comparing two countries in which the poor have equal real incomes, the one in which the rich are wealthier is likely to have a higher infant mortality rate.” He also explains reverse causality as a reason for this, implying that high infant mortality causes income inequality and also leads to faster growth of population, which in turn could give low wages especially to poor and increase income inequality.
In a study by Mahfuz (2008) they found that the socio- economic determinants like health expenditure, GDP per capita, education, etc. cannot always be considered to determine life expectancy in developing countries, so they suggested that countries should formulate policies and programs. They also concluded that total health expenditure has a significant influence on life expectancy as it directly affects the mortality. Lower expenditure on health increases infant mortality and GDP and health expenditure have a positive relation, because with increase in GDP the country will have more to spend on health expenses. According to Shaw, Horrace and Vogel (2005) there is a positive relation between per capita health expenditure and life expectancy at various ages for developed countries.
(1) To determine how changes in Birth Rate, Death Rate, Infant Mortality, GNP and Health Expenditure leads to changes in life expectancy?
(2)To determine how life expectancy is different in developing and developed countries?
(3)To draw conclusions about significance of the variables included in the analysis.
The influence of the independent variables on life expectancy will be established quantitatively. Adelman(1963, pp.315) quotes that “One advantage of this approach is that greater range of variation in characteristics among countries and less degree of interaction among explanatory variables permit much more acute determination of regression co-efficient.”
For analysing the links between life expectancy and some established demographic and economic variables the following model is chosen:
LE = + BR +DR + IM + GNP + HE + u
LE = Life Expectancy
BR = Birth Rate (No. of births per year per 1000 population in a country)
DR = Death Rate (No. of deaths per year per 1000 population in a country)
IM = Infant Mortality rate (No. of deaths of children from age one year and below
relative to no. of live births in a country)
GNP = Per Capita GNP (Gross National Product of a country divided by population)
HE = Per capita health expenditure (total health expenditure of a country divided by
= Behavioural characteristics
u = Stochastic error term
In the above model LE is the dependent variable and BR, DR, IM, GNP and HE are the independent variables. The model satisfies the assumptions regarding the distribution of error term. The above model is a combination of two models taken from Econometrics and data analysis for developing countries (Mukherjee, White and Wuyts, 2003) and ‘Econometrics analysis of life expectancy’ (Hendrickson, 1982).
The data will be obtained for 100 ‘Developed’ and ‘Developing’ countries and a cross-sectional regression analysis will be carried out from which subsequently some light will be shed on the nature of length of life in these countries. The raw data will be obtained from World Bank, World Development Indicators (WDI) April 2007, ESDS International, (MIMAS) University Of Manchester. The data will then be arranged in excel sheet and regression analysis will be done through statistical software’s: E-views and Stata.
A priori, it may be suggested that there will be a negative relation between death rate and infant mortality on life expectancy. With increase in death rate and infant mortality, it is expected that the life expectancy of a country will decrease. Birth rate might have a positive or negative influence on life expectancy, which will depend on the social and economic condition of the country. Also health expenditure and GNP can be expected to have a positive relation with life expectancy. As the health expenditure of a country increases, people will have better living conditions which will in turn affect life expectancy positively. With increase in GNP, the population will become richer and thus life expectancy will increase with increase in GNP.
The study is however subject to some limitations like non availability of data for certain countries. Developed and developing countries are taken into consideration, thus the results will be valid only for the countries taken into account for analysis. There might be other social and economic factors that have substantial influence in determining life expectancy of a country but have not been taken into consideration in this analysis. A single method research is adopted to carry out the analysis, which may increase concerns of validity and bias.
Proposed Timetable for the Project:
Work on word Proposal
3rd week of June
4th week of June
Run regression and Read Literature
1st week of July
3rd week of July
Draft and review EDA
4th week of July
1st week of Aug
Submission of EDA
Prepare presentation and start drafting final report
2nd week of Aug
3rd week of Aug
4th week of Aug
Proof reading of final report
4th week of Aug
1st week of Sep
Submit the final report
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