Gdp Per Capita And Life Expectancy example essay topic

The life expectancy at birth is the age in which an individual is expected to reach when they are born. The life expectancy varies across nations and continents and there may be a number of reasons for these variations. It is important that we become educated about positive and negative factors that are influencing life expectancy across countries so changes can be implemented. If we can begin to understand why life expectancy varies for different nations and why life expectancy is where it is today, we will be able to do a better job raising the living standards for all people in the future. The purpose of this study is to answer the underlying question: What are some factors that influence life expectancy across nations? Model Construction The data in this study was obtained from the U.S. Census Bureau, International Programs Center, International Database.

This study looks at two models that use cross-sectional data that was taken from 39 countries. There were three unusual observations within the data, one observation whose x value gave it a large influence and two observations with a large standardized residual. These observations were not omitted. The data is from 1999. The dependent variable of interest is life expectancy at birth within each nation. The observations are in years.

The independent variables for the analysis are as follows: GDP per Capita ( ) The gross domestic product per capita ( dollars) for each nation was observed and regressed against life expectancy to determine if this variable had any impact on life expectancy. The purchasing power parity or was used because it uses a common set of prices, those of the United States, to measure the output of each country. This provides a more accurate comparison of incomes for each country. One would expect that in countries with a higher GDP per capita, individuals would have a higher life expectancy. This expectation is based on the assumption that in countries with a higher GDP per capita, the income would be higher for the population. With a higher income, individuals would spend less of a percentage of income on food consumption and have more money left over for health care and education.

Therefore, a positive relationship would be expected between GDP per capita and life expectancy. The GDP per capita is measured in U.S. dollars. Unemployment Rate Unemployment rate was chosen as a variable in order to determine the effects of the economic conditions of a country on life expectancy. It is expected that in countries with economic prosperity (low unemployment), individuals will have a higher life expectancy.

If individuals are working, they are earning income. With income, individuals would have the ability to use some of that income to purchase health care, accurate food, water, and shelter, and a higher education. This expectation implies a negative relationship between the two variables, that is, the higher the unemployment the lower the life expectancy. The unemployment rate is given in percentage terms. Percentage of Jobs in Agriculture This variable is intended to determine the impact that the industrialization (or lack of industrialization) in a country has on life expectancy. Countries with a high percentage of the population working in agriculture are expected to have fewer jobs in manufacturing.

Countries with a large number of the population working in industry and manufacturing jobs are expected to have a higher national income and thus a higher life expectancy. A negative relationship between percentage of jobs in agriculture and life expectancy is expected, that is, the higher the percentage of jobs in agriculture, the lower the life expectancy rate. Continent A dummy variable was used to determine if a country's geographical location had any effect on life expectancy. Differences in this category could be attributed to weather differences or cultural differences. Each country was put into one of five categories. The five categories were South America, Asia, Europe, Africa, and North America.

Regression Analysis Two separate regressions were used. The first is a multiple regression that included GDP, unemployment rate, and percentage of jobs in agriculture. The second regression included the category variables in addition to the original three variables to examine any additional impact that geographical location might have on life expectancy. The coefficients and their accompanying t-statistics are found below. Regression Results Model 1 Model 2 Constant 80.938 78.925 (23.37) (8.40) GDP per capita -.

0000215 -. 0000057 (-. 13) (-. 03) Unemployment Rate -. 21416 -. 19336 (-2.87) (-2.30) % Jobs in Agriculture -.

32664 -. 30785 (-6.00) (-5.09) South America 2.719 (. 33) Asia 747 (. 09) Europe 2.345 (. 31) Africa -1.316 (-. 16) Adj. R-squired 75.3% 73.5% Results and Interpretation The results for the analysis are interesting.

As indicated by the t-statistics, the two variables that have a significant relationship with life expectancy are unemployment rate and percentage of jobs in agriculture. An F-test was used in comparing the additional impact, if any, the dummy variables had on the dependent variable. The additional impact was found to be insignificant. Although the GDP per capita variable was proved to be insignificant, it was include in the model to illustrate its lack of affect on life expectancy. A Breusch-Pagan test for heteroscedasticity was performed and the null hypothesis of homoscedasticity was accepted. A squired unemployment rate term was added in one of the regressions not included in the model.

This was done to test for a possible non-linear relationship. A non-linear relationship was not found and the term was left out of the final model. The most surprising result is the fact that there is no relationship between GDP per capita and life expectancy. This is evident by the weak t-statistic for this variable in both regressions.

A positive relationship was expected and no logical explanation can be made based on the data of this study. It would be interesting to research this matter further to determine why GDP per capita, a measure of income, does not seem to have a correlation with life expectancy. The relationship between the unemployment rate and life expectancy was a negative one as shown by the t-statistic. This negative relationship was expected. One might assume that this be the case since, when individuals are not working, less money is spent on health care, education, and technology.

Therefore, as unemployment changes, life expectancy changes in the opposite direction. The most interesting result is the very strong negative relationship between the percentage of jobs engaged in agriculture and life expectancy. This is shown by the strong t-statistic (-6.00). It can be hypothesized that the negative relationship may be due to agricultural societies having a less-skilled workforce, less technology and industrialization, a lower level of education, and thus, a lower level of income. These factors alone can hinder economic growth and result in lower health care, higher disease and more poverty-all factors that lower life expectancy.

The relationship between the continent and life expectancy showed no significant relationship, that is, geographical location does not have a significant impact on life expectancy. This is shown by the low t-statistics on all dummy variables. This is rather interesting and further research must be done with a larger sample before an accurate hypothesis on this lack of relationship between these two variables can be made. The adjusted R-squired is large for both models at 75.3% and 73.5% respectfully. This indicates that these models can explain a large part of the variation, about three quarter's worth in life expectancy.

Conclusion The intention of this study was to determine the impact of GDP per capita, unemployment rate, percentage of jobs in agriculture, and geographical location on life expectancy for different countries. Of the four expectations that were held prior to this study, two, the unemployment rate and the percentage of jobs agriculture, were proven to be accurate based on the results. The other two, GDP per capita and geographical location, were proven to be inaccurate based on the results. It is highly likely that variations in life expectancy is dependent on other factors relating to economic growth and development; however, in this study, this research has revealed two variables that may explain for variations in life expectancy. This study has also raised some important questions: If GDP per capita is not related with life expectancy, which income variables are?

Is the savings and investment rate of a country's population related to life expectancy? What about trade and exchange rate policies? What impact do they have? If geography is not related to life expectancy, are natural resource endowments related? What important variables are being overlooked in this study? Does a country's religion or ethnic background have anything to do with life expectancy?

How about political variations, what role do they play? This study cannot answer these questions, but this study can be a beginning for future research and analysis that can educate us on what predictors influence life expectancy. Then we may begin to make changes that may lessen the variations in life expectancy between countries.