Purpose of analysis
Several studies have investigated the covariates of life expectancy at birth (Kozilek, Sorana & Iulia, 2017; Mahyar, 2016). Data on life expectancy at birth and GDP per capita was drawn from 50 countries across the world. This analysis investigates the relationship between these variables in order to gather more knowledge of the already existing literature on determinants of life expectancy.
Does GDP per Capita predict life expectancy at birth?
The variables being investigated are GDP per capita as the independent variable and life expectancy at birth as the dependent variable
Independent variable - GDP per capita refers to the value of all goods and services produced in a country in a given year divided by the mid-year population
Dependent variable - Life expectancy at birth refers t length of time in years a person is expected to live, from the time of birth.
To be able to answer the research question, a brief literature review is conducted in order to find out what other work has been done relating to the research problem of interest in the present study. Following this, the data to be analyzed is gathered. In this case, data on GDP per capita and life expectancy at birth from 50 countries across the world was gathered from various internet sources including See http://en.wikipedia.org/wiki/.
Once data is obtained, a choice is made on what statistical tools would be useful to answer the research question. An important consideration is on the type of variable in terms of how it was measured (Frankfort-Nachmias & Leon-Guerrero, 2015, 43). Numerical variable would need to be analyzed using statistical tools different from categorical variables. Both the independent and dependent variables in the present research are numerical. The research question aims at determining the relationship between the variables. Being continuous variables, a Pearson Correlation analysis is used to determine if an association exists between the two variables as well as the strength and direction of the association (Wagner, 2016. 23). Further, the analysis aims at determining whether GDP per capita predicts life expectancy at birth. An appropriate test to use is linear regression analysis, which is applicable when one intends to predict the value of one variable based on the value of another (others) (Warner, 2012. 160). In the present case, only one independent variable (GDP per capita) is considered, hence a simple linear regression analysis is used.
Before carrying out any statistical analysis on data, the data has to be checked to ensure that all the assumptions for a given test are met. For linear regression analysis the data has to meet several assumptions namely: the variables have to be on a continuous scale, there should not be any significant outliers in the data, there should be normal distribution of errors within the data set, homescdasticity is met, meaning that distances between data points and the best line of fit are similar and the relationship between the variables must be linear. Although the data had a few outliers, it was expected that they would not cause undue influence on the model given that the value of cooks distance obtained was between 0.0 and 0.2, which is below the general rule of 1. Given this, no data points were removed in this analysis.
In order to describe the data, means were obtained for the variables. The mean life expectancy at birth was 72.6 years while the mean GDP per capita was 19552USD. To determine the strength and direction of the relationship between GDP per capita and life expectancy at birth, a Pearson correlation analysis was run. The correlation was found to be 0.73, p < 0.001, as shown below. This suggests a statistically significant positive and strong relationship between the two variables. This means that as GDP per capita increases so does life expectancy at birth.
GDP Pearson Correlation 1 .730**
Sig. (2-tailed) .000
N 50 50
LIFEEx Pearson Correlation .730** 1
Sig. (2-tailed) .000 N 50 50
**. Correlation is significant at the 0.01 level (2-tailed).
A trend analysis involving the two variables reveals a positive linear exponential trend with life expectancy increasing as GDP per capita increased, as shown in the scatter chart below. The best line of fit is also plotted in the chart which also shows the equation of the line.
The general line equation is given as follows: Y = Mx + c, where m represents the slope of the line and C is the Y intercept, or the point at which the best line of fit crosses the y axis.
As indicated the equation of the line is:
Y = 0.00043x + 64.23
In this case the slope (gradient) of the line is 0.00043 while the y intercept is 64.23. In this context, GDP per capita begins to affect life expectancy after 64.23 years.
The scatter chat below shows a cluster of points and a number of outliers. Grouping is also evident around GDP between 10, 000USD and 20, 000 USD and between 30, 000 USD and 40, 000USD. The scatter plot also shows homescdasticity, distances of data points from the best line of fit are similar, despite the few cases of outliers.
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .730a .533 .523 5.725
a. Predictors: (Constant), GDP b. Dependent Variable: LIFEEx The model summary above gives a sense of the model fit of the data. A strong correlation coefficient of 0.73 between the two variables is indicated. It also shows R2 (coefficient of determination) of 53.3, which means that 53% of the variance in life expectancy at birth is explained by GDP per capita.
Model Sum of Squares df Mean Square F Sig.
1 Regression 1795.034 1 1795.034 54.770 .000a
Residual 1573.146 48 32.774 Total 3368.180 49 a. Predictors: (Constant), GDP b. Dependent Variable: LIFEEx The ANOVA table above shows that the model is a good fit for the data (p< 0.001) and GDP per capita significantly predicts life expectancy at birth, F(48) = 54.77, p>0.001
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta 1 (Constant) 64.231 1.389 46.255 .000
GDP .00043 .000 .730 7.401 .000
a. Dependent Variable: LIFEEx The regression model can be obtained from the table of coefficients above as follows
Y = Mx + C: Life expectancy at birth = MGDP per capita + C
Life expectancy at birth = 64.23 + 0.00043GDP per capita
From the regression model above the prediction is that for every unit increase in Gross Domestic Product per capita, life expectancy at birth will increase by 0.00043 years.
From the analysis of the sample it can be concluded that at the population level, life expectancy at birth will increase by 0.00043 for every unit increase in GDP per capita. This finding is consistent with those of other researchers who found similar results (Kozilek et al., 2017, 70; Mahyar, 2016. 85). However, as literature shows, GDP is not the only covariate in respect to life expectancy at birth. Many other determinants play a role including infrastructure index, economy index, urban city, employment status and poverty index (Mahyar, 2016. 85).
That these findings are based on data obtained on various internet sites, it is not immediately clear what methods were used to obtain the figures. This may affect both the internal and external validity of our findings. Nevertheless, these findings are similar to those found by other researchers and there is nothing unusual about them.
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Kozilek, Thabitta- Wanda, et al. "Measuring Welfare in Romania: Alternative and Complementary Measures to Gross Domestic Product. Regional Formation & Development Studies, no. 22, May 2017, pp. 68-76. EBSCOhost, doi:10.15181/rfds.v22i2.1478.
Mahyar, Hami. "Economic Growth and Life Expectancy: The Case of Iran." Studies in Business & Economics, vol. 11, no. 1, Apr. 2016, pp. 80-87. EBSCOhost, doi:10.1515/sbe-2016-0007.
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